% Keywords: Adaptive Streaming
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@InProceedings{Tashtarian2021,
  author = {Farzad Tashtarian and Abdelhak Bentaleb and Reza Farahani and Minh Nguyen and Christian Timmerer and Hellwagner, Hermann and Roger Zimmermann},
  booktitle = {2021 IEEE 46th Conference on Local Computer Networks (LCN)},
  title = {{A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP}},
  year = {2021},
  month = {oct},
  pages = {162--169},
  publisher = {IEEE},
  abstract = {Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGC systems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA (joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an appropriate solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance of QuaLA in achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutions.},
  doi = {10.1109/lcn52139.2021.9525027},
  keywords = {UGC streaming, low latency live streaming, fairness, QoE, HAS, DASH, ABR, adaptive streaming, ADMM},
  url = {https://ieeexplore.ieee.org/document/9525027}
}
@InProceedings{Taraghi2021b,
  author = {Babak Taraghi},
  booktitle = {Proceedings of the 29th {ACM} International Conference on Multimedia},
  title = {{End-to-end Quality of Experience Evaluation for HTTP Adaptive Streaming}},
  year = {2021},
  month = {oct},
  pages = {2936--2939},
  publisher = {ACM},
  abstract = {Exponential growth in multimedia streaming traffic over the Internet motivates the research and further investigation of the user's perceived quality of such services. Enhancement of experienced quality by the users becomes more substantial when service providers compete on establishing superiority by gaining more subscribers or customers. Quality of Experience (QoE) enhancement would not be possible without an authentic and accurate assessment of the streaming sessions. HTTP Adaptive Streaming (HAS) is today's prevailing technique to deliver the highest possible audio and video content quality to the users. An end-to-end evaluation of QoE in HAS covers the precise measurement of the metrics that affect the perceived quality, eg. startup delay, stall events, and delivered media quality. Mentioned metrics improvements could limit the service's scalability, which is an important factor in real-world scenarios. In this study, we will investigate the stated metrics, best practices and evaluations methods, and available techniques with an aim to (i) design and develop practical and scalable measurement tools and prototypes, (ii) provide a better understanding of current technologies and techniques (eg. Adaptive Bitrate algorithms), (iii) conduct in-depth research on the significant metrics in a way that improvements of QoE with scalability in mind would be feasible, and finally (iv) provide a comprehensive QoE model which outperforms state-of-the-art models.},
  doi = {10.1145/3474085.3481025},
  keywords = {HTTP Adaptive Streaming, Quality of Experience, Subjective Evaluation, Objective Evaluation, Adaptive Bitrate, QoE model},
  url = {https://dl.acm.org/doi/10.1145/3474085.3481025}
}
@Article{Taraghi2021a,
  author = {Babak Taraghi and Minh Nguyen and Hadi Amirpour and Christian Timmerer},
  journal = {IEEE Access},
  title = {{Intense: In-Depth Studies on Stall Events and Quality Switches and Their Impact on the Quality of Experience in {HTTP} Adaptive Streaming}},
  year = {2021},
  issn = {2169-3536},
  month = aug,
  pages = {118087--118098},
  volume = {9},
  abstract = {With the recent growth of multimedia traffic over the Internet and emerging multimedia streaming service providers, improving Quality of Experience (QoE) for HTTP Adaptive Streaming (HAS) becomes more important. Alongside other factors, such as the media quality, HAS relies on the performance of the media player’s Adaptive Bitrate (ABR) algorithm to optimize QoE in multimedia streaming sessions. QoE in HAS suffers from weak or unstable internet connections and suboptimal ABR decisions. As a result of imperfect adaptiveness to the characteristics and conditions of the internet connection, stall events and quality level switches could occur and with different durations that negatively affect the QoE. In this paper, we address various identified open issues related to the QoE for HAS, notably (i) the minimum noticeable duration for stall events in HAS; (ii) the correlation between the media quality and the impact of stall events on QoE; (iii) the end-user preference regarding multiple shorter stall events versus a single longer stall event; and (iv) the end-user preference of media quality switches over stall events. Therefore, we have studied these open issues from both objective and subjective evaluation perspectives and presented the correlation between the two types of evaluations. The findings documented in this paper can be used as a baseline for improving ABR algorithms and policies in HAS.},
  doi = {10.1109/access.2021.3107619},
  keywords = {Crowdsourcing, HTTP adaptive streaming, quality of experience, quality switches, stall events, subjective evaluation, objective evaluation},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  url = {https://ieeexplore.ieee.org/document/9521894}
}
@InProceedings{Taraghi2021,
  author = {Babak Taraghi and Abdelhak Bentaleb and Christian Timmerer and Roger Zimmermann and Hellwagner, Hermann},
  booktitle = {Proceedings of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video},
  title = {{Understanding quality of experience of heuristic-based HTTP adaptive bitrate algorithms}},
  year = {2021},
  month = {jul},
  pages = {82--89},
  publisher = {ACM},
  abstract = {Adaptive bitrate (ABR) algorithms play a crucial role in delivering the highest possible viewer's Quality of Experience (QoE) in HTTP Adaptive Streaming (HAS). Online video streaming service providers use HAS - the dominant video streaming technique on the Internet - to deliver the best QoE for their users. A viewer's delight relies heavily on how the ABR of a media player can adapt the stream's quality to the current network conditions. QoE for video streaming sessions has been assessed in many research projects to give better insight into the significant quality metrics such as startup delay and stall events. The ITU Telecommunication Standardization Sector (ITU-T) P.1203 quality evaluation model allows to algorithmically predict a subjective Mean Opinion Score (MOS) by considering various quality metrics. Subjective evaluation is the best assessment method for examining the end-user opinion over a video streaming session's experienced quality. We have conducted subjective evaluations with crowdsourced participants and evaluated the MOS of the sessions using the ITU-T P.1203 quality model. This paper's main contribution is to investigate the correspondence of subjective and objective evaluations for well-known heuristic-based ABRs.},
  doi = {10.1145/3458306.3458875},
  keywords = {HTTP Adaptive Streaming, ABR Algorithms, Quality of Experience, Crowdsourcing, Subjective Evaluation, Objective Evaluation, MOS},
  url = {https://dl.acm.org/doi/10.1145/3458306.3458875}
}
@InProceedings{Menon2021a,
  author = {Vignesh V Menon and Hadi Amirpour and Mohammad Ghanbari and Christian Timmerer},
  booktitle = {2021 IEEE International Conference on Image Processing (ICIP)},
  title = {{Efficient Content-Adaptive Feature-Based Shot Detection for HTTP Adaptive Streaming}},
  year = {2021},
  month = {sep},
  pages = {2174--2178},
  publisher = {IEEE},
  abstract = {Video delivery over the Internet has been becoming a commodity in recent years, owing to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of segments. This paper focuses on segmenting video into multiple shots for encoding in Video on Demand (VoD) HTTP Adaptive Streaming (HAS) applications. Therefore, we propose a novel Discrete Cosine Transform (DCT) feature-based shot detection and successive elimination algorithm for shot detection and compare it against the default shot detection algorithm of the x265 implementation of the High Efficiency Video Coding (HEVC) standard. Our experimental results demonstrate that our proposed feature-based pre-processor has a recall rate of 25% and an F-measure of 20% greater than the benchmark algorithm for shot detection.},
  doi = {10.1109/icip42928.2021.9506092},
  keywords = {HTTP Adaptive Streaming, Video-on-Demand, Shot detection, multi-shot encoding},
  url = {https://ieeexplore.ieee.org/document/9506092}
}
@InProceedings{Menon2021,
  author = {Vignesh Menon and Hadi Amirpourazarian and Christian Timmerer and Mohammad Ghanbari},
  booktitle = {2021 Picture Coding Symposium (PCS)},
  title = {{Efficient Multi-Encoding Algorithms for HTTP Adaptive Bitrate Streaming}},
  year = {2021},
  month = jun,
  pages = {1--5},
  publisher = {IEEE},
  abstract = {Since video accounts for the majority of today’s internet traffic, the popularity of HTTP Adaptive Streaming (HAS) is increasing steadily. In HAS, each video is encoded at multiple bitrates and spatial resolutions (i.e., representations) to adapt to a heterogeneity of network conditions, device characteristics, and end-user preferences. Most of the streaming services utilize cloud-based encoding techniques which enable a fully parallel encoding process to speed up the encoding and consequently to reduce the overall time complexity. State-of-the-art approaches further improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to improve the encoding time complexity of the remaining representations. In this paper, we investigate various multi-encoding algorithms (i.e., multi-rate and multi-resolution) and propose novel multi- encoding algorithms for large-scale HTTP Adaptive Streaming deployments. Experimental results demonstrate that the proposed multi-encoding algorithm optimized for the highest compression efficiency reduces the overall encoding time by 39% with a 1.5% bitrate increase compared to stand-alone encodings. Its optimized version for the highest time savings reduces the overall encoding time by 50% with a 2.6% bitrate increase compared to stand-alone encodings.},
  doi = {10.1109/pcs50896.2021.9477499},
  keywords = {HTTP Adaptive Streaming, HEVC, Multi-rate Encoding, Multi-encoding},
  url = {https://ieeexplore.ieee.org/document/9477499}
}
@InProceedings{Farahani2021a,
  author = {Reza Farahani and Farzad Tashtarian and Hadi Amirpour and Christian Timmerer and Mohammad Ghanbari and Hellwagner, Hermann},
  booktitle = {2021 IEEE 46th Conference on Local Computer Networks (LCN)},
  title = {{CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming}},
  year = {2021},
  month = {oct},
  pages = {525--532},
  publisher = {IEEE},
  abstract = {Recent studies have revealed that network-assisted techniques, by providing a comprehensive view of the network, improve HTTP Adaptive Streaming (HAS) system performance significantly. This paper leverages the capability of Software-Defined Networking, Network Function Virtualization, and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming (CSDN) framework. We employ virtualized edge entities to collect various information items and run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers. In case of a cache miss, a client’s request is served by an optimal replacement quality from a cache server, by a quality transcoded from an optimal replacement quality at the edge, or by the originally requested quality from the origin server. Comprehensive experiments conducted on a large-scale testbed demonstrate that CSDN outperforms other approaches in terms of the users’ QoE and network utilization.},
  doi = {10.1109/lcn52139.2021.9524970},
  keywords = {Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE), Software Defined Networking (SDN), Network Function Virtualization (NFV), Video Transcoding, Content Delivery Network (CDN)},
  url = {https://ieeexplore.ieee.org/document/9524970}
}
@InProceedings{Farahani2021,
  author = {Reza Farahani and Farzad Tashtarian and Alireza Erfanian and Christian Timmerer and Mohammad Ghanbari and Hellwagner, Hermann},
  booktitle = {Proceedings of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video},
  title = {{ES-HAS: an edge- and SDN-assisted framework for HTTP adaptive video streaming}},
  year = {2021},
  month = {jul},
  pages = {50--57},
  publisher = {ACM},
  abstract = {Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, clients have full control over the media streaming and adaptation processes. Lack of coordination among the clients and lack of awareness of the network conditions may lead to sub-optimal user experience and resource utilization in a pure client-based HAS adaptation scheme. Software Defined Networking (SDN) has recently been considered to enhance the video streaming process. In this paper, we leverage the capability of SDN and Network Function Virtualization (NFV) to introduce an edge- and SDN-assisted video streaming framework called ES-HAS. We employ virtualized edge components to collect HAS clients' requests and retrieve networking information in a time-slotted manner. These components then perform an optimization model in a time-slotted manner to efficiently serve clients' requests by selecting an optimal cache server (with the shortest fetch time). In case of a cache miss, a client's request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, or (ii) by the original requested quality level from the origin server. This approach is validated through experiments on a large-scale testbed, and the performance of our framework is compared to pure client-based strategies and the SABR system [12]. Although SABR and ES-HAS show (almost) identical performance in the number of quality switches, ES-HAS outperforms SABR in terms of playback bitrate and the number of stalls by at least 70% and 40%, respectively.},
  doi = {10.1145/3458306.3460997},
  keywords = {Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE), Software Defined Networking (SDN), Network Function Virtualization (NFV)},
  url = {https://dl.acm.org/doi/10.1145/3458306.3460997}
}
@Article{Erfanian2021,
  author = {Alireza Erfanian and Farzad Tashtarian and Anatoliy Zabrovskiy and Christian Timmerer and Hellwagner, Hermann},
  journal = {IEEE Transactions on Network and Service Management},
  title = {{OSCAR: On Optimizing Resource Utilization in Live Video Streaming}},
  year = {2021},
  issn = {1932-4537},
  month = {mar},
  number = {1},
  pages = {552--569},
  volume = {18},
  abstract = {Live video streaming traffic and related applications have experienced significant growth in recent years. However, this has been accompanied by some challenging issues, especially in terms of resource utilization. Although IP multicasting can be recognized as an efficient mechanism to cope with these challenges, it suffers from many problems. Applying software-defined networking (SDN) and network function virtualization (NFV) technologies enable researchers to cope with IP multicasting issues in novel ways. In this article, by leveraging the SDN concept, we introduce OSCAR (Optimizing reSourCe utilizAtion in live video stReaming) as a new cost-aware video streaming approach to provide advanced video coding (AVC)-based live streaming services in the network. In this article, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder function (VTF). At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, by executing a mixed-integer linear program (MILP), the SDN controller determines a group of optimal multicast trees for streaming the requested videos from an appropriate origin server to the VRPs. Moreover, to elevate the efficiency of resource allocation and meet the given end-to-end latency threshold, OSCAR delivers only the highest requested quality from the origin server to an optimal group of VTFs over a multicast tree. The selected VTFs then transcode the received video segments and transmit them to the requesting VRPs in a multicast fashion. To mitigate the time complexity of the proposed MILP model, we present a simple and efficient heuristic algorithm that determines a near-optimal solution in polynomial time. Using the MiniNet emulator, we evaluate the performance of OSCAR in various scenarios. The results show that OSCAR surpasses other SVC- and AVC-based multicast and unicast approaches in terms of cost and resource utilization.},
  doi = {10.1109/tnsm.2021.3051950},
  keywords = {Dynamic adaptive streaming over HTTP (DASH), live video streaming, software defined networking (SDN), video transcoding, network function virtualization (NFV)},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  url = {https://ieeexplore.ieee.org/document/9327491}
}
@Article{Cetinkaya2021a,
  author = {Ekrem Cetinkaya and Hadi Amirpour and Christian Timmerer and Mohammad Ghanbari},
  journal = {IEEE Open Journal of Signal Processing},
  title = {{Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning}},
  year = {2021},
  issn = {2644-1322},
  month = jun,
  pages = {1--12},
  abstract = {Video streaming applications keep getting more attention over the years, and HTTP Adaptive Streaming (HAS) became the de-facto solution for video delivery over the Internet. In HAS, each video is encoded at multiple quality levels and resolutions (i.e., representations) to enable adaptation of the streaming session to viewing and network conditions of the client. This requirement brings encoding challenges along with it, e.g., a video source should be encoded efficiently at multiple bitrates and resolutions. Fast multi-rate encoding approaches aim to address this challenge of encoding multiple representations from a single video by re-using information from already encoded representations. In this paper, a convolutional neural network is used to speed up both multi-rate and multi-resolution encoding for HAS. For multi-rate encoding, the lowest bitrate representation is chosen as the reference. For multi-resolution encoding, the highest bitrate from the lowest resolution representation is chosen as the reference. Pixel values from the target resolution and encoding information from the reference representation are used to predict Coding Tree Unit (CTU) split decisions in High-Efficiency Video Coding (HEVC) for dependent representations. Experimental results show that the proposed method for multi-rate encoding can reduce the overall encoding time by 15.08 % and parallel encoding time by 41.26 %, with a 0.89 % bitrate increase compared to the HEVC reference software. Simultaneously, the proposed method for multi-resolution encoding can reduce the encoding time by 46.27 % for the overall encoding and 27.71 % for the parallel encoding on average with a 2.05 % bitrate increase.},
  doi = {10.1109/ojsp.2021.3078657},
  keywords = {HTTP Adaptive Streaming, HEVC, Multirate Encoding, Machine Learning},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  url = {https://ieeexplore.ieee.org/document/9427195}
}
@InProceedings{AguilarArmijo2021,
  author = {Jesus Aguilar-Armijo and Christian Timmerer and Hellwagner, Hermann},
  booktitle = {2021 IEEE 46th Conference on Local Computer Networks (LCN)},
  title = {{EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming}},
  year = {2021},
  month = {oct},
  pages = {487--494},
  publisher = {IEEE},
  abstract = {Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.},
  doi = {10.1109/lcn52139.2021.9524883},
  keywords = {Edge Computing, HTTP Adaptive Streaming, Network-assisted Video Streaming, Quality of Experience},
  url = {https://ieeexplore.ieee.org/document/9524883}
}
@InProceedings{Zabrovskiy2020,
  author = {Anatoliy Zabrovskiy and Prateek Agrawal and Roland Matha and Christian Timmerer and Radu Prodan},
  booktitle = {2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)},
  title = {{ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Sequences Using Artificial Neural Network}},
  year = {2020},
  month = sep,
  pages = {316--325},
  publisher = {{IEEE}},
  abstract = {HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state of the art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22\% compared to the state of the art.},
  doi = {10.1109/bigmm50055.2020.00056},
  keywords = {Transcoding time prediction, adaptive streaming, video transcoding, neural networks, video encoding, video complexity class, HTTP adaptive streaming, MPEG-DASH},
  url = {https://ieeexplore.ieee.org/document/9232616}
}
@InProceedings{VenkataPhaniKumar2020,
  author = {Venkata Phani Kumar Malladi and Christian Timmerer and Hellwagner, Hermann},
  booktitle = {2020 IEEE International Conference on Multimedia and Expo (ICME)},
  title = {{Mipso: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming}},
  year = {2020},
  month = {jul},
  pages = {1--6},
  publisher = {IEEE},
  abstract = {Video delivery over the Internet has become more and more established in recent years due to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The current DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of periods, adaptation sets, representations and segments. Although multi-period MPDs are widely used in live streaming scenarios, they are not fully utilized in Video-on-Demand (VoD) HTTP adaptive streaming (HAS) scenarios. In this paper, we introduce MiPSO, a framework for Multi–Period per-Scene Optimization, to examine multiple periods in VoD HAS scenarios. MiPSO provides different encoded representations of a video at either (i) maximum possible quality or (ii) minimum possible bitrate, beneficial to both service providers and subscribers. In each period, the proposed framework adjusts the video representations (resolution-bitrate pairs) by taking into account the complexities of the video content, with the aim of achieving streams at either higher qualities or lower bitrates. The experimental evaluation with a test video data set shows that the MiPSO reduces the average bitrate of streams with the same visual quality by approximately 10% or increases the visual quality of streams by at least 1 dB in terms of Peak Signal-to-Noise (PSNR) at the same bitrate compared to conventional approaches to video content delivery.},
  doi = {10.1109/icme46284.2020.9102775},
  keywords = {Adaptive Streaming, Video-on-Demand, Per-Scene Encoding, Media Presentation Description},
  url = {https://ieeexplore.ieee.org/document/9102775}
}
@InProceedings{Timmerer2020,
  author = {Christian Timmerer and Hellwagner, Hermann},
  booktitle = {Proceedings of the Brazilian Symposium on Multimedia and the Web},
  title = {{HTTP Adaptive Streaming: Where Is It Heading?}},
  year = {2020},
  month = {nov},
  pages = {349--350},
  publisher = {ACM},
  abstract = {In this contribution, we present selected novel approaches and results of our research work in the ATHENA Christian Doppler Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services), a major research project at our department jointly funded by public sources and industry. By putting this work also into the context of related ongoing research activities, we aim at working out where HTTP Adaptive Streaming is currently heading.},
  doi = {10.1145/3428658.3434574},
  keywords = {HTTP adaptive streaming, video coding, machine learning, edge computing, immersive media, quality of experience},
  url = {https://dl.acm.org/doi/10.1145/3428658.3434574}
}
@InProceedings{Taraghi2020,
  author = {Babak Taraghi and Anatoliy Zabrovskiy and Christian Timmerer and Hellwagner, Hermann},
  booktitle = {Proceedings of the 11th ACM Multimedia Systems Conference},
  title = {{Cloud-based Adaptive Video Streaming Evaluation Framework for the Automated Testing of Media Players CAdViSE}},
  year = {2020},
  month = {may},
  pages = {349--352},
  publisher = {ACM},
  abstract = {Attempting to cope with fluctuations of network conditions in terms of available bandwidth, latency and packet loss, and to deliver the highest quality of video (and audio) content to users, research on adaptive video streaming has attracted intense efforts from the research community and huge investments from technology giants. How successful these efforts and investments are, is a question that needs precise measurements of the results of those technological advancements. HTTP-based Adaptive Streaming (HAS) algorithms, which seek to improve video streaming over the Internet, introduce video bitrate adaptivity in a way that is scalable and efficient. However, how each HAS implementation takes into account the wide spectrum of variables and configuration options, brings a high complexity to the task of measuring the results and visualizing the statistics of the performance and quality of experience. In this paper, we introduce CAdViSE, our Cloud-based Adaptive Video Streaming Evaluation framework for the automated testing of adaptive media players. The paper aims to demonstrate a test environment which can be instantiated in a cloud infrastructure, examines multiple media players with different network attributes at defined points of the experiment time, and finally concludes the evaluation with visualized statistics and insights into the results.},
  doi = {10.1145/3339825.3393581},
  keywords = {HTTP Adaptive Streaming, Media Players, MPEG-DASH, Network Emulation, Automated Testing, Quality of Experience},
  url = {https://dl.acm.org/doi/10.1145/3339825.3393581}
}
@InProceedings{Nguyen2020a,
  author = {Minh Nguyen and Christian Timmerer and Hellwagner, Hermann},
  booktitle = {Proceedings of the 25th ACM Workshop on Packet Video},
  title = {{H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive Video Streaming}},
  year = {2020},
  month = {jun},
  pages = {1--7},
  publisher = {ACM},
  abstract = {HTTP-based Adaptive Streaming (HAS) plays a key role in over-the-top video streaming. It contributes towards reducing the rebuffering duration of video playout by adapting the video quality to the current network conditions. However, it incurs variations of video quality in a streaming session because of the throughput fluctuation, which impacts the user’s Quality of Experience (QoE). Besides, many adaptive bitrate (ABR) algorithms choose the lowest-quality segments at the beginning of the streaming session to ramp up the playout buffer as soon as possible. Although this strategy decreases the startup time, the users can be annoyed as they have to watch a low-quality video initially. In this paper, we propose an efficient retransmission technique, namely H2BR, to replace low-quality segments being stored in the playout buffer with higher-quality versions by using features of HTTP/2 including (i) stream priority, (ii) server push, and (iii) stream termination. The experimental results show that H2BR helps users avoid watching low video quality during video playback and improves the user’s QoE. H2BR can decrease by up to more than 70% the time when the users suffer the lowest-quality video as well as benefits the QoE by up to 13%.},
  doi = {10.1145/3386292.3397117},
  keywords = {HTTP adaptive streaming, DASH, ABR algorithms, QoE, HTTP/2},
  url = {https://dl.acm.org/doi/abs/10.1145/3386292.3397117}
}
@InProceedings{Nguyen2020,
  author = {Minh Nguyen and Hadi Amirpour and Christian Timmerer and Hellwagner, Hermann},
  booktitle = {Proceedings of the Workshop on the Evolution, Performance, and Interoperability of QUIC},
  title = {{Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC}},
  year = {2020},
  month = {aug},
  pages = {28--34},
  publisher = {ACM},
  abstract = {HTTP/2 has been explored widely for adaptive video streaming, but still suffers from Head-of-Line blocking, and three-way handshake delay due to TCP. Meanwhile, QUIC running on top of UDP can tackle these issues. In addition, although many adaptive bitrate (ABR) algorithms have been proposed for scalable and non-scalable video streaming, the literature lacks an algorithm designed for both types of video streaming approaches. In this paper, we investigate the impact of QUIC and HTTP/2 on the performance of ABR algorithms. Moreover, we propose an efficient approach for utilizing scalable video coding formats for adaptive video streaming that combines a traditional video streaming approach (based on non-scalable video coding formats) and a retransmission technique. The experimental results show that QUIC benefits significantly from our proposed method in the context of packet loss and retransmission. Compared to HTTP/2, it improves the average video quality and provides a smoother adaptation behavior. Finally, we demonstrate that our proposed method originally designed for non-scalable video codecs also works efficiently for scalable videos such as Scalable High Efficiency Video Coding (SHVC).},
  doi = {10.1145/3405796.3405829},
  keywords = {QUIC, H2BR, HTTP adaptive streaming, Retransmission, SHVC},
  url = {https://dl.acm.org/doi/10.1145/3405796.3405829}
}
@InProceedings{Hooft2020,
  author = {Jeroen van der Hooft and Maria Torres Vega and Christian Timmerer and Ali C. Begen and Filip De Turck and Raimund Schatz},
  booktitle = {2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)},
  title = {{Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming}},
  year = {2020},
  month = {may},
  publisher = {IEEE},
  abstract = {Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide economically. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user’s motion within the scene. We determine which of these aspects has more impact on the user’s QoE, and to what extent subjective and objective assessments are aligned.},
  doi = {10.1109/qomex48832.2020.9123081},
  keywords = {Volumetric Media, HTTP Adaptive Streaming, 6DoF, MPEG V-PCC, QoE Assessment, Objective Metrics},
  url = {https://ieeexplore.ieee.org/document/9123081}
}
@InProceedings{Erfanian2020,
  author = {Alireza Erfanian and Farzad Tashtarian and Reza Farahani and Christian Timmerer and Hellwagner, Hermann},
  booktitle = {2020 6th IEEE Conference on Network Softwarization (NetSoft)},
  title = {{On Optimizing Resource Utilization in AVC-based Real-time Video Streaming}},
  year = {2020},
  month = {jun},
  pages = {301--309},
  publisher = {IEEE},
  abstract = {Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and additional computational complexity. The emerging software-defined networking (SDN)and network function virtualization (NFV) paradigms enable re-searchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC)-based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF)functions. At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requested VRPs over unicast paths. To mitigate the time complexity of the proposed MILPmodel, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using theMiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.},
  doi = {10.1109/netsoft48620.2020.9165450},
  keywords = {Dynamic Adaptive Streaming over HTTP (DASH), Real-time Video Streaming, Software Defined Networking (SDN), Video Transcoding, Network Function Virtualization (NFV)},
  url = {https://ieeexplore.ieee.org/document/9165450}
}
@InProceedings{Cetinkaya2020,
  author = {Ekrem Cetinkaya and Hadi Amirpour and Christian Timmerer and Mohammad Ghanbari},
  booktitle = {2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)},
  title = {{FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning}},
  year = {2020},
  month = {dec},
  pages = {87--90},
  publisher = {{IEEE}},
  abstract = {HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. The requirement to encode the same content at different quality levels (i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.},
  doi = {10.1109/vcip49819.2020.9301850},
  keywords = {HEVC, Multirate Encoding, Machine Learning, DASH, HTTP Adaptive Streaming, HAS},
  url = {https://ieeexplore.ieee.org/abstract/document/9301850}
}
@InProceedings{Amirpour_2020,
  author = {Hadi Amirpour and Ekrem Cetinkaya and Christian Timmerer and Mohammad Ghanbari},
  booktitle = {2020 Data Compression Conference (DCC)},
  title = {{Fast Multi-rate Encoding for Adaptive HTTP Streaming}},
  year = {2020},
  month = {mar},
  publisher = {IEEE},
  abstract = {Adaptive HTTP streaming is the preferred method to deliver multimedia content in the internet. It provides multiple representations of the same content in different qualities (i.e. bit-rates and resolutions) and allows the client to request segments from the available representations in a dynamic, adaptive way depending on its context. The growing number of representations in adaptive HTTP streaming makes encoding of one video segment at different representations a challenging task in terms of encoding time-complexity. In this paper, information of both highest and lowest quality representations are used to limit Rate Distortion Optimization (RDO) for each Coding Unit Tree (CTU) in High Efficiency Video Coding. Our proposed method first encodes the highest quality representation and consequently uses it to encode the lowest quality representation. In particular, the block structure and the selected reference frame of both highest and lowest quality representations are then used to predict and shorten the RDO process of each CTU for intermediate quality representations. Our proposed method introduces a delay of two CTUs thanks to employing parallel processing techniques. Experimental results show significant reduction in time-complexity over the reference software 38% and the state-of-the-art 10% while quality degradation is negligible.},
  doi = {10.1109/dcc47342.2020.00080},
  keywords = {HTTP adaptive streaming, Multi-rate encoding, HEVC, Fast block partitioning},
  url = {https://ieeexplore.ieee.org/document/9105709}
}
@InProceedings{Amirpour2020,
  author = {Hadi Amirpour and Christian Timmerer and Mohammad Ghanbari},
  booktitle = {2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)},
  title = {{Towards View-Aware Adaptive Streaming of Holographic Content}},
  year = {2020},
  month = {jul},
  publisher = {IEEE},
  abstract = {Holography is able to reconstruct a three-dimensional structure of an object by recording full wave fields of light emitted from the object. This requires a huge amount of data to be encoded, stored, transmitted, and decoded for holographic content, making its practical usage challenging especially for bandwidth-constrained networks and memory-limited devices. In the delivery of holographic content via the internet, bandwidth wastage should be avoided to tackle high bandwidth demands of holography streaming. For real-time applications, encoding time-complexity is also a major problem. In this paper, the concept of dynamic adaptive streaming over HTTP (DASH) is extended to holography image streaming and view-aware adaptation techniques are studied. As each area of a hologram contains information of a specific view, instead of encoding and decoding the entire hologram, just the part required to render the selected view is encoded and transmitted via the network based on the users’ interactivity. Four different strategies, namely, monolithic, single view, adaptive view, and non-real time streaming strategies are explained and compared in terms of bandwidth requirements, encoding time-complexity, and bitrate overhead. Experimental results show that the view-aware methods reduce the required bandwidth for holography streaming at the cost of a bitrate increase.},
  doi = {10.1109/icmew46912.2020.9106055},
  keywords = {Holography, compression, bitrate adaption, dynamic adaptive streaming over HTTP, DASH},
  url = {https://ieeexplore.ieee.org/document/9106055}
}
@InProceedings{AguilarArmijo2020,
  author = {Jesus Aguilar-Armijo and Babak Taraghi and Christian Timmerer and Hellwagner, Hermann},
  booktitle = {2020 IEEE International Symposium on Multimedia (ISM)},
  title = {{Dynamic Segment Repackaging at the Edge for {HTTP} Adaptive Streaming}},
  year = {2020},
  month = {dec},
  pages = {17--24},
  publisher = {IEEE},
  abstract = {Adaptive video streaming systems typically support different media delivery formats, e.g., MPEG-DASH and HLS, replicating the same content multiple times into the network. Such a diversified system results in inefficient use of storage, caching, and bandwidth resources. The Common Media Application Format (CMAF) emerges to simplify HTTP Adaptive Streaming (HAS), providing a single encoding and packaging format of segmented media content and offering the opportunities of bandwidth savings, more cache hits and less storage needed. However, CMAF is not yet supported by most devices. To solve this issue, we present a solution where we maintain the main advantages of CMAF while supporting heterogeneous devices using different media delivery formats. For that purpose, we propose to dynamically convert the content from CMAF to the desired media delivery format at an edge node. We study the bandwidth savings with our proposed approach using an analytical model and simulation, resulting in bandwidth savings of up to 20% with different media delivery format distributions.

We analyze the runtime impact of the required operations on the segmented content performed in two scenarios: the classic one, with four different media delivery formats, and the proposed scenario, using CMAF-only delivery through the network. We compare both scenarios with different edge compute power assumptions. Finally, we perform experiments in a real video streaming testbed delivering MPEG-DASH using CMAF content to serve a DASH and an HLS client, performing the media conversion for the latter one.},
  doi = {10.1109/ism.2020.00009},
  keywords = {CMAF, Edge Computing, HTTP Adaptive Streaming (HAS)}
}
@Article{Agrawal2020,
  author = {Prateek Agrawal and Anatoliy Zabrovskiy and Adithyan Ilangovan and Christian Timmerer and Radu Prodan},
  journal = {Cluster Computing},
  title = {{FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos}},
  year = {2020},
  issn = {1573-7543},
  month = {nov},
  pages = {1--17},
  abstract = {HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.},
  doi = {10.1007/s10586-020-03207-x},
  keywords = {Transcoding time prediction, Video transcoding, Scheduling, Artificial neural networks, MPEG-DASH, Adaptive streaming},
  publisher = {Springer Science and Business Media LLC},
  url = {https://link.springer.com/article/10.1007/s10586-020-03207-x}
}
@Article{Rainer2016,
  author = {Rainer, Benjamin and Posch, Daniel and Hellwagner, Hermann},
  journal = {Journal on Selected Areas in Communications},
  title = {Investigating the Performance of Pull-based Dynamic Adaptive Streaming in NDN},
  year = {2016},
  issn = {1558-0008},
  month = {aug},
  number = {8},
  pages = {11},
  volume = {34},
  abstract = {Adaptive content delivery is the state-of-the-art in real-time multimedia streaming. Leading streaming approaches, e.g., MPEG-DASH and Apple HLS, have been developed for classical IP-based networks, providing effective streaming by means of pure client-based control and adaptation. However, the research activities of the Future Internet community adopt a new course that is different from today's host-based communication model. So-called Information-Centric Networks are of considerable interest and are advertised as enablers for intelligent networks, where effective content delivery is to be provided as an inherent network feature. This paper investigates the performance gap between pure client-driven adaptation and the theoretical optimum in the promising Future Internet architecture Named Data Networking (NDN). The theoretical optimum is derived by modeling multimedia streaming in NDN as a fractional Multi-Commodity Flow Problem and by extending it taking caching into account. We investigate the multimedia streaming performance under different forwarding strategies, exposing the interplay of forwarding strategies and adaptation mechanisms. Furthermore, we examine the influence of network inherent caching on the streaming performance by varying the caching polices and the cache sizes.},
  address = {New York, USA},
  doi = {10.1109/JSAC.2016.2577365},
  keywords = {Information-Centric Networking; Named Data Networking; Multimedia; Dynamic Adaptive Streaming.},
  language = {EN},
  pdf = {https://www.itec.aau.at/bib/files/jsac.pdf},
  publisher = {IEEE}
}
@InProceedings{HH2015a,
  author = {Hellwagner, Hermann and Kacianka, Severin},
  booktitle = {MoVid '15 Proceedings of the 7th ACM International Workshop on Mobile Video},
  title = {Adaptive Video Streaming for UAV Networks},
  year = {2015},
  address = {New York, USA},
  editor = {Halvorsen, Pal and Dutt, Nikil},
  month = {mar},
  pages = {25-30},
  publisher = {ACM International Conference on Multimedia Systems},
  abstract = {The core problem for any adaptive video streaming solution, particularly over wireless networks, is the detection (or even prediction) of congestion. IEEE 802.11 is especially vulnerable to fast movement and change of antenna orientation. When used in UAV networks (Unmanned Aerial Vehicles), the network throughput can vary widely and is almost impossible to predict. this paper evaluates an approach originally developed by Kofler for home networks, in a single-hop UAV wireless network setting: the delay between the sending of an IEEE 802.11 packet and the receipt of its corresponding acknowledgement is used as an early indicator of the link quality and as a trigger to adapt (reduce or increase) the video stream' s bitrate. Our real-world flight-tests indicate, that this avoids congestion and can frequently avoid the complete loss of video pictures which happens without adaptation.},
  doi = {10.1145/2727040.2727043},
  isbn13 = {978-1-4503-3353-5},
  keywords = {Video Streaming, Adaptive Streaming, UAVs, UAV Communication},
  language = {EN},
  location = {Portland, OR, USA},
  talktype = {none}
}
@InProceedings{TimmererBegen2014,
  author = {Timmerer, Christian and Begen, Ali Cengiz},
  booktitle = {Proceedings of the 2014 ACM Multimedia Conference},
  title = {Over the Top Content Delivery: State of the Art and Challenges Ahead},
  year = {2014},
  address = {New York, NY, USA},
  editor = {Hua, Kien and Rui, Yong and Steinmetz, Ralf and Hanjalic, Alan and Natsev, Apostol and Zhu, Wenwu},
  month = {nov},
  pages = {1231--1232},
  publisher = {ACM},
  abstract = {In this tutorial we present state of the art and challenges ahead in over-the-top content delivery. It particular, the goal of this tutorial is to provide an overview of adaptive media delivery, specifically in the context of HTTP adaptive streaming (HAS) including the recently ratified MPEG-DASH standard. The main focus of the tutorial will be on the common problems in HAS deployments such as client design, QoE optimization, multi-screen and hybrid delivery scenarios, and synchronization issues. For each problem, we will examine proposed solutions along with their pros and cons. In the last part of the tutorial, we will look into the open issues and review the work-in-progress and future research directions.},
  doi = {10.1145/2647868.2654849},
  isbn13 = {978-1-4503-3063-3},
  keywords = {adaptive media streaming, dynamic adaptive streaming over HTTP, MPEG-DASH, over-the-top video video},
  language = {EN},
  location = {Orlando, FL, USA},
  pdf = {https://www.itec.aau.at/bib/files/tut02-timmerer.pdf},
  slides = {https://www.itec.aau.at/bib/files/ACM_MM_Tutorial_11_2014.pdf},
  talkdate = {2014.11.03},
  talktype = {registered}
}
@InProceedings{Rainer2014_ACM_MM_SELFORG,
  author = {Rainer, Benjamin and Timmerer, Christian},
  booktitle = {Proceedings of the 22st ACM International Conference on Multimedia},
  title = {Self-Organized Inter-Destination Multimedia Synchronization For Adaptive Media Streaming},
  year = {2014},
  address = {New York, NY, USA},
  editor = {ACM,},
  month = {nov},
  pages = {10},
  publisher = {ACM},
  abstract = {Social networks have become pervasive and have changed the way of social interaction. The traditional TV experience drifts from an event tied to a certain place with the family or friends to a location-independent and distributed social experience. Additionally, more and more video on-demand services adopt a pull-based streaming approach. In order to provide a synchronized and distributed TV experience we introduce a self-organized Inter-Destination Multimedia Synchronization (IDMS) framework for adaptive media streaming. In particular, we extend the principles of IDMS to adaptive media streaming over HTTP (i.e., MPEG-DASH) and enable a synchronized multimedia playback among geographically distributed clients. Therefore, we introduce session management to MPEG-DASH and for negotiating on a reference playback timestamp among the participating peers in an IDMS session we propose a distributed control scheme. We evaluate our proposed scheme with respect to scalability and time required for negotiating on the reference playback timestamp. Furthermore, we investigate how to compensate the identified asynchronism by using adaptive media playout with respect to the Quality of Experience (QoE). Therefore, we define a temporal distortion measure for audio and video which allows us to model the impact of playback rate variations on the QoE. This measure is evaluated by conducting a subjective quality assessment using crowdsourcing.},
  isbn13 = {-},
  keywords = {Inter-Destination Multimedia Synchronization, Adaptive Media Streaming, Self-Organization, Quality of Experience, Dynamic Adaptive Streaming over HTTP},
  language = {EN},
  location = {Orlando, Florida},
  pdf = {https://www.itec.aau.at/bib/files/acmm14.pdf},
  talkdate = {2014.11.03},
  talktype = {registered},
  url = {http://acmmm.org/2014/}
}
@InProceedings{Rain1412:VNext,
  title = {Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-World Environments using Crowdsourcing},
  author = {Rainer, Benjamin and Timmerer, Christian},
  booktitle = {First International Workshop on VideoNext: Design, Quality and Deployment of Adaptive Video Streaming},
  year = {2014},
  address = {Australia, Sydney},
  editor = {N, N},
  month = {dec},
  pages = {1-6},
  publisher = {ACM},
  keywords = {Dynamic Adaptive Streaming over HTTP; Crowdsourcing; Subjective Quality Assessment; Quality of Experience; QoE; DASH; MPEG},
  language = {EN},
  location = {Singapore},
  pdf = {https://www.itec.aau.at/bib/files/videoNextDASH.pdf},
  talkdate = {2014.12.02},
  talktype = {registered}
}
@InProceedings{Posch2014b,
  author = {Posch, Daniel and Kreuzberger, Christian and Rainer, Benjamin and Hellwagner, Hermann},
  booktitle = {Proceedings of the 10th International Conference on Emerging Networking Experiments and Technologies, VideoNext Workshop},
  title = {Using In-Network Adaptation to Tackle Inefficiencies Caused by DASH in Information-Centric Networks},
  year = {2014},
  address = {New York, NY, USA},
  editor = {Dixon, Colin},
  month = {dec},
  pages = {1-6},
  publisher = {ACM Digital Library},
  abstract = {The consumption of audio-visual content is the most dominant traffic source in today's Internet. Novel architectural approaches, such as Information-Centric Networking (ICN), are developed to support efficient multimedia dissemination. As ICN and MPEG-DASH have several concepts in common, recent proposals consider a fusion of both technologies. However, MPEG-DASH relies on pure client-driven adaptation. This often rather selfish adaptation strategy inhibits benefits gained from ICN's inherent caching and multi-path transmission capabilities. In order to overcome this challenge, the contribution of this work is the integration of in-network adaptation (INA) in ICN. We illustrate that INA can be realized despite ICN's content-based security model. Our proposal rests on scalable content, which enables INA without management and transmission overhead.},
  keywords = {Information-Centric Networking; In-Network Adaptation; Adaptive Streaming; Multimedia Dissemination},
  language = {EN},
  location = {Sydney, Australia},
  pdf = {https://www.itec.aau.at/bib/files/video01fp.pdf},
  talkdate = {2014.12.02},
  talktype = {registered}
}
@InProceedings{Posch2014,
  author = {Posch, Daniel and Kreuzberger, Christian and Rainer, Benjamin and Hellwagner, Hermann},
  booktitle = {Proceedings of the 1st ACM Conference on Information-Centric Networking},
  title = {Client Starvation: A Shortcoming of Client-driven Adaptive Streaming in Named Data Networking},
  year = {2014},
  address = {New York, NY, USA},
  editor = {Mendes, Paulo},
  month = {sep},
  pages = {1-2},
  publisher = {ACM Digital Library},
  abstract = {Information-centric Networking (ICN) as a potential Future Internet architecture has to efficiently support the consumption of multimedia content. Recent proposals consider the reuse of MPEG-DASH to provide adaptive streaming in ICN. Due to the fact that MPEG-DASH relies on pure client-driven adaptation, it encounters difficulties dealing with ICN's inherent caching and multi-path transmission. By conducting simulations using the concrete ICN approach Named Data Networking (NDN), we show that pure client-driven adaptation leads to shortcomings. Furthermore, we propose to use in-network adaptation based on scalable content for overcoming these shortcomings in NDN.},
  doi = {10.1145/2660129.2660162},
  keywords = {Information-centric Networking; Adaptive Streaming},
  language = {EN},
  location = {Paris, Frankreich},
  pdf = {https://www.itec.aau.at/bib/files/icn14_final.pdf},
  talkdate = {2014.09.26},
  talktype = {poster},
  url = {http://dx.doi.org/10.1145/2660129.2660162}
}
@InProceedings{Posch2013,
  author = {Posch, Daniel and Hellwagner, Hermann and Schartner, Peter},
  booktitle = {Proceedings of the 8th International Workshop on Secure Network Protocols (NPSec' 13)},
  title = {On-Demand Video Streaming based on Dynamic Adaptive Encrypted Content Chunks},
  year = {2013},
  address = {Los Alamitos, CA, USA},
  editor = {Li, Jun and Maennel, Olaf},
  month = {oct},
  pages = {6},
  publisher = {IEEE},
  abstract = {This paper proposes a framework for on-demand video streaming that enables secure and efficient delivery of data towards the end user. Our proposal requires the combined usage of three different technologies. The first one is a recent proposal by Jacobsen et al. called Content-Centric Networking (also known as Named Data Networking). It is a network architecture that introduces named data as the most valuable element in the network and divides it into so called content chunks, which are self-identifying and self-authenticating data units. The second concept we utilize derives from the approach of Dynamic Adaptive Streaming over HTTP, which allows clients to dynamically choose the quality of the received video stream according to their available resources. Finally, we adapt the concept of Broadcast Encryption to form a tool to control the access to provided content streams. The combination of these technologies enables us to design a framework that allows streaming providers to transport data to customers as dynamic adaptive encrypted content chunks, which is an efficient, flexible and scalable way of multimedia data transport.},
  keywords = {Content-Centric Networking, CCN, Named Data Networking, NDN, Dynamic Adaptive Streaming, DASH, Broadcast Encryption, Video on Demand},
  language = {EN},
  location = {Germany, Göttingen},
  pdf = {https://www.itec.aau.at/bib/files/ICNP_NPSEC_Streaming.pdf},
  talkdate = {2013.10.07},
  talktype = {registered}
}
@Article{Mueller2013_MMC,
  author = {Mueller, Christopher and Lederer, Stefan and Timmerer, Christian},
  journal = {IEEE Multimedia Communications Technical Committee E-Letter},
  title = {Fair Share Dynamic Adaptive Streaming over HTTP},
  year = {2013},
  month = {mar},
  number = {2},
  pages = {30-33},
  volume = {8},
  abstract = {Multimedia delivery over the Hypertext Transfer Protocol (HTTP) is currently very popular and with MPEGs' Dynamic Adaptive Streaming over HTTP (DASH) a standard is available to provide interoperability and enable large-scale deployments using existing infrastructures (servers, proxies, caches, etc.). This paper identifies some issue when multiple DASH clients compete for a bandwidth bottleneck when transparent proxy caches are deployed. Therefore, we propose a fair share adaptation scheme to be included within the client which – through experimental results – achieve a more efficient utilization of the bottleneck bandwidth and less quality switches.},
  address = {New York, NY, USA},
  keywords = {Dynamic Adaptive Streaming over HTTP, DASH, Fair Adaptation, Proxy Cache, Multimedia},
  language = {EN},
  pdf = {https://www.itec.aau.at/bib/files/E-Letter-March13.pdf},
  publisher = {IEEE Communications Society [online]},
  url = {http://committees.comsoc.org/mmc/e-news/E-Letter-March13.pdf}
}
@InProceedings{Liu2013,
  author = {Liu, Yaning and Geurts, Joost and Point, Jean-Charles and Lederer, Stefan and Rainer, Benjamin and Mueller, Christopher and Timmerer, Christian and Hellwagner, Hermann},
  booktitle = {Proceedings of the IEEE international Conference on Communication (ICC) 2013 – Next-Generation Networking Symposium},
  title = {Dynamic Adaptive Streaming over CCN: A Caching and Overhead Analysis},
  year = {2013},
  address = {Budapest},
  editor = {Mattheisen, Christopher and Murase, Tutomu},
  month = {jun},
  pages = {2222-2226},
  publisher = {IEEE},
  abstract = {In this paper, we present our implementation and evaluation of Dynamic Adaptive Streaming over Content centric networking (DASC) which implements MPEG Dynamic Adaptive Streaming over HTTP (DASH) utilizing a Content Centric Networking (CCN) naming scheme to identify content segments in a CCN network. In particular, video segments formatted according to MPEG-DASH are available in different quality levels but instead of HTTP, CCN is used for referencing and delivery. Based on the conditions of the network, the DASC client issues interests for segments achieving the best throughput. Due to segment caching within the network, subsequent requests for the same content can be served quicker. As a result, the quality of the video a user receives progressively improves, effectively overcoming bottlenecks in the network. We present two sets of experiments to evaluate the performance of DASC showing that throughput indeed improves. However, the generated overhead is relatively large and the adaptation strategy used for DASH that assumes an end-to-end connection could be revised for the hop-by-hop architecture of CCN.},
  keywords = {Content Centric Networking, Dynamic Adaptive Streaming, HTTP Video Streaming, MPEG-DASH},
  language = {EN},
  location = {Budapest, Hungary},
  pdf = {https://www.itec.aau.at/bib/files/ICC2013 -DASH Over CCN.PDF},
  talkdate = {2013.06.11},
  talktype = {registered},
  url = {http://www.ieee-icc.org}
}
@InProceedings{Lederer2013b,
  author = {Lederer, Stefan and Mueller, Christopher and Rainer, Benjamin and Timmerer, Christian and Hellwagner, Hermann},
  booktitle = {In Proceedings of the IEEE ICC'13 - Workshop on Immersive \& Interactive Multimedia Communications over the Future Internet},
  title = {Adaptive Streaming over Content Centric Networks in Mobile Networks using Multiple Links},
  year = {2013},
  address = {Budapest},
  editor = {Assuncao, Pedro and Atzori, Luigi and Dagiuklas, Tasos and Kondoz, Ahmet},
  month = {jun},
  pages = {687-691},
  publisher = {IEEE},
  abstract = {This paper presents the usage of Content Centric Networking (CCN) for adaptive multimedia streaming in mobile environments, leveraging the recent ISO/IEC MPEG Dynamic Adaptive Streaming over HTTP (DASH) standard. The performance of DASH over CCN is evaluated using real-world mobile bandwidth traces and compared to previous evaluations of different DASH-based as well as proprietary systems. As there are no client-server connections in CCN, it offers the possibility to transfer data from multiple sources as well as over multiple links in parallel, which is definitely an important feature, e.g., for mobile devices offering multiple network links. This functionality is used and evaluated in this paper in combination with DASH, making it possible to dynamically choose the best performing link for media streaming, which is a clear advantage over DASH using HTTP and the TCP/IP protocol stack. The evaluation therefore investigates DASH over CCN in two scenarios using synthetic and real-world mobile bandwidth traces respectively, showing a significantly better performance than conventional DASH using only one connection.},
  keywords = {MPEG-DASH, CCN, Dynamic Adaptive Streaming over HTTP, Content Centric Networking, Evaluation},
  language = {EN},
  location = {Budapest, Hungary},
  pdf = {https://www.itec.aau.at/bib/files/ICC2013_Mobile_DASHoverCCN.pdf},
  talkdate = {2013.06.13},
  talktype = {registered},
  url = {http://multicomm.diee.unica.it/}
}
@InProceedings{Lederer2013a,
  author = {Lederer, Stefan and Mueller, Christopher and Timmerer, Christian and Concolato, Cyril and Le Feuvre, Jean and Fliegel, Karel},
  booktitle = {Proceedings of the 4th ACM Multimedia Systems Conference},
  title = {Distributed DASH Dataset},
  year = {2013},
  address = {New York, NY, USA},
  editor = {Griwodz, Carsten},
  month = {feb},
  pages = {pp. 131-135},
  publisher = {ACM},
  abstract = {The delivery of multimedia content over HTTP and on top of existing Internet infrastructures is becoming the preferred method within heterogeneous environment. The basic design principle is having an intelligent client which selects given and applicable media representations by issuing HTTP requests for individual segments based on the users' context and current conditions. Typically, this client behavior differs between implementations of the same kind and for the objective evaluations thereof appropriate datasets are needed. This paper presents a distributed dataset for the recently published MPEG-DASH standard which is mirrored at different sites across Europe, namely Klagenfurt, Paris, and Prague. A client implementation may choose to request segments from these sites and dynamically switch to a different location, e.g., in case the one currently used causes any issues. Thus, this distributed DASH dataset can be used for real-world evaluations enabling the simulation of switching between different content delivery networks.},
  keywords = {Dataset, Dynamic Adaptive Streaming over HTTP, DASH.},
  language = {EN},
  location = {Oslo, Norway},
  pdf = {https://www.itec.aau.at/bib/files/MMSys_CDN_Simulation_Dataset_v2.0.pdf},
  talkdate = {2013.02.27},
  talktype = {registered},
  url = {http://www.mmsys.org/}
}
@InProceedings{Grafl2013_PQS,
  author = {Grafl, Michael and Timmerer, Christian},
  booktitle = {Proceedings of the 4th International Workshop on Perceptual Quality of Systems ({PQS} 2013)},
  title = {Representation Switch Smoothing for Adaptive {HTTP} Streaming},
  year = {2013},
  address = {Vienna, Austria},
  editor = {Schatz,Raimund and Hoßfeld, Tobias},
  month = {sep},
  pages = {178-183},
  publisher = {FTW},
  abstract = {When an adaptive media streaming system has to switch from one representation of the content to another, the switch causes viewer distraction. We introduce the concept of representation switch smoothing for alleviating the distraction and improving the overall quality of experience. As adaptive HTTP streaming systems typically deploy video buffers on the client side, the adaptation decision is known far enough ahead of playout time to perform a seamless transition between quality representations. We discuss implementation considerations for an adaptive HTTP streaming system with scalable video coding, present a subjective evaluation of the proposed approach, and identify factors that influence how smooth transitions are perceived.},
  keywords = {adaptive streaming; representation switching; quality of experience},
  language = {EN},
  location = {Vienna, Austria},
  pdf = {https://www.itec.aau.at/bib/files/representation_switch_smoothing.pdf},
  talkdate = {2013.09.04},
  talktype = {registered}
}
@InProceedings{Mueller2012b,
  author = {Mueller, Christopher and Renzi, Daniele and Lederer, Stefan and Battista, Stefano and Timmerer, Christian},
  booktitle = {Proceedings of the 20th European Signal Processing Conference (EUSIPCO12)},
  title = {Using Scalable Video Coding for Dynamic Adaptive Streaming over HTTP in Mobile Environments},
  year = {2012},
  address = {Bucharest, Romania},
  editor = {Burileanu, Corneliu and Pesquet-Popescu, Béatrice},
  month = {aug},
  pages = {2208-2212},
  publisher = {European Signal Processing (EURASIP) Society},
  abstract = {Dynamic Adaptive Streaming over HTTP (DASH) is a convenient approach to transfer videos in an adaptive and dynamic way to the user. As a consequence, this system provides high bandwidth flexibility and is especially suitable for mobile use cases where the bandwidth variations are tremendous. In this paper we have integrated the Scalable Video Coding (SVC) extensions of the Advanced Video Coding (AVC) standard into the recently ratified MPEG-DASH standard. Furthermore, we have evaluated our solution under restricted conditions using bandwidth traces from mobile environments and compared it with an improved version of our MPEG-DASH implementation using AVC as well as major industry solutions.},
  keywords = {Dynamic Adaptive Streaming over {HTTP}, {MPEG-DASH}, Scalable Video Coding, Evaluation, Mobile Networks, Vehicular Mobility},
  language = {EN},
  location = {Bucharest, Romania},
  pdf = {https://www.itec.aau.at/bib/files/mueller_svc-dash.pdf},
  talkdate = {2012.08.31},
  talktype = {registered}
}
@InProceedings{Mueller2012a,
  author = {Mueller, Christopher and Lederer, Stefan and Timmerer, Christian},
  booktitle = {Proceedings of the Fourth Annual ACM SIGMM Workshop on Mobile Video (MoVid12)},
  title = {An Evaluation of Dynamic Adaptive Streaming over HTTP in Vehicular Environments},
  year = {2012},
  address = {New York, NY, USA},
  editor = {Hefeeda, Mohamed and Hsu, Cheng-Hsin and Chatterjee, Mainak and Venkatasubramanian, Nalini and Ganguly, Samrat},
  month = {feb},
  pages = {37-42},
  publisher = {ACM},
  abstract = {MPEGs' Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is an emerging standard designed for media delivery over the top of existing infrastructures and able to handle varying bandwidth conditions during a streaming session. This requirement is very important, specifically within mobile environments and, thus, DASH could potentially become a major driver for mobile multimedia streaming. Hence, this paper provides a detailed evaluation of our implementation of MPEG DASH compared to the most popular propriety systems, i.e., Microsoft Smooth Steaming, Adobe HTTP Dynamic Streaming, and Apple HTTP Live Streaming. In particular, these systems will be evaluated under restricted conditions which are due to vehicular mobility. In anticipation of the results, our prototype implementation of MPEG-DASH can very well compete with state-of-the-art solutions and, thus, can be regarded as a mature standard ready for industry adaption.},
  keywords = {Dynamic Adaptive Streaming over HTTP, MPEG-DASH, Microsoft Smooth Streaming, Adobe HTTP Dynamic Streaming, Evaluation, Apple HTTP Live Streaming, Mobile Networks, Vehicular Mobility},
  language = {EN},
  location = {Chapel Hill, North Carolina, USA},
  pdf = {https://www.itec.aau.at/bib/files/p37-mueller.pdf},
  talkdate = {2012.02.24},
  talktype = {registered}
}
@InProceedings{Mueller2012,
  author = {Lederer, Stefan and Mueller, Christopher and Timmerer, Christian},
  booktitle = {Proceedings of the Third Annual {ACM SIGMM} Conference on Multimedia Systems ({MMSys12})},
  title = {Dynamic Adaptive Streaming over {HTTP} Dataset},
  year = {2012},
  address = {New York, NY, USA},
  editor = {Claypool, Mark and Griwodz, Carsten and Mayer-Patel, Ketan},
  month = {feb},
  pages = {89-94},
  publisher = {ACM},
  abstract = {Adaptive HTTP streaming got lot of attention in recent years and with dynamic adaptive streaming over HTTP (DASH) a standard is available. Many papers cover this topic and present their research results, but unfortunately all of them use their own private dataset which – in most cases – is not publicly available. Hence, it is difficult to compare, e.g., adaptation algorithms in an objective way due to the lack of a common dataset which shall be used as basis for such experiments. In this paper, we present our DASH dataset featuring our DASHEncoder, an open source DASH content generation tool. We also provide basic evaluations of the different segment lengths, the influence of HTTP server settings, and, in this context, we show some of the advantages as well as problems of shorter segment lengths.},
  keywords = {Dynamic Adaptive Streaming over HTTP, DASH, Dataset, Encoder, Content Generation Tool},
  language = {EN},
  location = {Chapel Hill, North Carolina, USA},
  pdf = {https://www.itec.aau.at/bib/files/p89-lederer.pdf},
  talkdate = {2012.02.22},
  talktype = {registered}
}
@InProceedings{Lederer2012,
  author = {Lederer, Stefan and Mueller, Christopher and Timmerer, Christian},
  booktitle = {Proceedings of the 19th International Packet Video Workshop ({PV} 2012)},
  title = {Towards Peer-Assisted Dynamic Adaptive Streaming over HTTP},
  year = {2012},
  address = {Munich, Germany},
  editor = {Guillemot, Christine and Chakareski, Jacob and Steinbach, Eckehard},
  month = {may},
  pages = {1-6},
  publisher = {IEEE},
  abstract = {This paper presents our peer-assisted Dynamic Adaptive Streaming over HTTP (pDASH) proposal as well as an evaluation based on our DASH simulation environment in comparison to conventional approaches, i.e., non-peer-assisted DASH. Our approach maintains the standard conformance to MPEG-DASH enabling an easy and straightforward way of enhancing a streaming system with peer assistance to reduce the bandwidth and infrastructure requirements of the content/service provider. In anticipation of the results our system achieves a bandwidth reduction of Content Distribution Networks (CDN) and as a consequence the corresponding infrastructure costs of the content/service providers by up to 25% by leveraging the upstream capacity of neighboring peers. Furthermore, the cost savings have been evaluated using a cost model that is based on the current Amazon CloudFront pricing scheme. Furthermore, we have also evaluated the performance impact that various combinations of quality levels of the content could have in a peer-assisted streaming system as well as the client behavior in such an environment.},
  keywords = {Peer-Assisted Streaming, MPEG-DASH, Dynamic Adaptive Streaming over HTTP, CDN Bandwidth Reduction, Peer-to-Peer Streaming.},
  language = {EN},
  location = {Munich, Germany},
  pdf = {https://www.itec.aau.at/bib/files/Paper53.pdf},
  talkdate = {2012.05.10},
  talktype = {registered}
}
@InProceedings{Mueller2011_ACMMM,
  author = {Mueller, Christopher and Timmerer, Christian},
  booktitle = {Proceedings of the 19th ACM international conference on Multimedia},
  title = {A VLC media player plugin enabling dynamic adaptive streaming over HTTP},
  year = {2011},
  address = {New York, NY, USA},
  editor = {Candan, Kasim Selcuk and Panchanathan, Sethuraman and Prabhakaran, Balakrishnan and Sundaram, Hari and Feng, Wu-Chi and Sebe, Nicu},
  month = {nov},
  pages = {723--726},
  publisher = {ACM},
  series = {MM},
  abstract = {This paper describes the implementation of a VLC media player plugin enabling Dynamic Adaptive Streaming over HTTP (DASH). DASH is an emerging ISO/IEC MPEG and 3GPP standard for HTTP streaming. It aims to standardize formats enabling segmented progressive download by exploiting existing Internet infrastructure as such. Our implementation of these formats as described in this paper is based on the well-known VLC. Hence, it is fully integrated into the VLC structure and has been also submitted to the VLC development team for consideration in future releases of VLC. Therefore, it is licensed under the GNU Lesser General Public License (LGPL). The plugin provides a very flexible structure that could be easily extended with respect to different adaptation logics or profiles of the DASH standard. As a consequence, the plugin enables the integration of a variety of adaptation logics and comparison thereof, making it attractive for the research community.},
  doi = {10.1145/2072298.2072429},
  keywords = {3GPP, DASH, HTTP streaming, MPEG, dynamic adaptive streaming over HTTP, video},
  language = {EN},
  location = {Scottsdale, Arizona, USA},
  pdf = {https://www.itec.aau.at/bib/files/p723-muller.pdf},
  talkdate = {2011.11.29},
  talktype = {registered}
}