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.

Abstract: In the last few years, delivery of immersive video with six degrees of freedom (6DoF) has become an important topic for content providers. Recent technological advancements have resulted in affordable head-mounted displays, allowing a broad range of users to enjoy Virtual Reality (VR) content. Service providers such as Facebook1and YouTube2were among the first to provide 360°video, using the principle of HTTP Adaptive Streaming (HAS) to deliver the content to the enduser. In HAS, the content is encoded using several quality representations, temporally segmented into chunks of one to ten seconds and stored on one or multiple servers within a content delivery network. Based on the perceived network conditions, the device characteristics, and the user's preferences, the client can then decide on the quality of each of these segments[1]. Having the ability to adapt the video quality, this approach actively avoids buffer starvation, and therefore results in smoother playback of the requested content and a higher Quality of Experience (QoE) for the end user[2]. The introduction of 360° video provides the user with three degrees of freedom to move within an immersive world, allowing changes in the yaw, roll, and pitch.In the last few years, multiple solutions have been proposed to efficiently deliver VR content through HAS, focusing, for instance, on foveas-and tile-based encoding, improved viewport prediction (i.e., prediction of the user’s head movement in the near future in order to buffer useful high-quality content), and application layer optimizations [3]. In these works, however, the location of the user remains fixed to the position of the camera within the scene. Recently, significant research efforts have been made to realize 6DoF for streamed video content, i.e., the user may experience three additional degrees of freedom by being able to change the viewing position in a video scene. These efforts are promising, but significant research contributions will be required in order to realize its full potential. In this paper, an overview of existing 6DoF solutions is presented, and key challenges and opportunities are highlighted.

Abstract: People use social media (SM) to describe and discuss different situations they are involved in, like crises. It is therefore worthwhile to exploit SM contents to support crisis management, in particular by revealing useful and unknown information about the crises in real-time. Hence, we propose a novel active online multiple-prototype classifier, called AOMPC. It identifies relevant data related to a crisis. AOMPC is an online learning algorithm that operates on data streams and which is equipped with active learning mechanisms to actively query the label of ambiguous unlabeled data. The number of queries is controlled by a fixed budget strategy. Typically, AOMPC accommodates partly labeled data streams. AOMPC was evaluated using two types of data: (1) synthetic data and (2) SM data from Twitter related to two crises, Colorado Floods and Australia Bushfires. To provide a thorough evaluation, a whole set of known metrics was used to study the quality of the results. Moreover, a sensitivity analysis was conducted to show the effect of AOMPC's parameters on the accuracy of the results. A comparative study of AOMPC against other available online learning algorithms was performed. The experiments showed very good behavior of AOMPC for dealing with evolving, partly-labeled data streams.

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Abstract: The popularity of computer games and e-sports is enormously high and still growing every year. Despite the popularity computer games often rely on old technologies, especially in the field of networking. Research in networking for games is challenging due to the low availability of up-todate datasets and network traces. In order to achieve a high user satisfaction while keeping the network activity as low as possible, modern networking solutions of computer games take players’ activities as well as closeness of players in the game world into account. In this paper, we analyze the Battle Royale game mode of the online multiplayer game Fortnite, where 100 players challenge each other in a king-of-the-hill like game within a constantly contracting game world, as an example for a popular online game with demanding technical requirements. We extrapolate player movement patterns by finding player positions automatically from videos, uploaded by Fortnite players on popular streaming platforms and show, how they influence network traffic from the client to the server and vice versa. This extended abstract features the highlights of [1], which has been accepted at the NetGames 2018 event.

Abstract: When developing new approaches in networking research, one of the most important requirements is to evaluate the degree of improvement of a new approach both realistically and cost-effectively. Wireless networks and their adequate emulation play an important role in evaluation, but emulation of wireless links and networks is still difficult to handle. In this paper, we present a low-cost, fixed-network testbed able to emulate the dynamically changing conditions of wireless links caused by client mobility and physical phenomena. We extend the existing fixed-network testbed for the purpose of wireless network emulation using the Linux tools tc, iptables, and NetEm in sophisticated ways. Convenient function blocks are provided to configure wireless network topologies as well as dynamic link and mobility conditions to be emulated with modest efforts. We utilize the testbed's capabilities to investigate the influence of different mobility models on streaming SVC-encoded videos in Named Data Networking (NDN), a novel Information-Centric Networking architecture. Furthermore, we evaluate the benefits of using early loss detection mechanisms for streaming in NDN, by implementing Wireless Loss Detection and Recovery (WLDR). Our results show that the extended fixed-network testbed can precisely emulate wireless network conditions and usage. For instance, the emulation revealed that both the choice of the mobility model and the use of WLDR have a substantial influence on the resulting SVC video streaming performance.

Abstract: Recent research in the field of Information-Centric Networking (ICN) shows the need for push-based data transfer, which is not supported in current pull-based ICN architectures, such as Named Data Networking (NDN). IoT deployments as well as emergency notifications and real-time multimedia communication are well suited to be realized using the ICN principles, but experience challenges in pull-based environments. Persistent Interests (PIs) are a promising approach to introduce pushlike traffic in Interest-based ICN architectures such as NDN. In this paper, we explore the characteristics of PIs and discuss advantages and disadvantages of using them. We provide an efficient solution for preventing so-called Data loops, which are introduced by giving up NDN’s one-request-per-packet principle. Furthermore, we investigate the performance of PIs compared to classical Interests in terms of the computational complexity of forwarding and discuss possible applications of PIs.

Abstract: Small drones are being utilized in monitoring, transport, safety and disaster management, and other domains. Envisioning that drones form autonomous networks incorporated into the air traffic, we describe a high-level architecture for the design of a collaborative aerial system consisting of drones with on-board sensors and embedded processing, coordination, and networking capabilities. We implement a multi-drone system consisting of quadcopters and demonstrate its potential in disaster assistance, search and rescue, and aerial monitoring. Furthermore, we illustrate design challenges and present potential solutions based on the lessons learned so far.

Abstract: Classification of evolving data streams is a challenging task, which is suitably tackled with online learning approaches. Data is processed instantly requiring the learning machinery to (self-)adapt by adjusting its model. However for high velocity streams, it is usually difficult to obtain labeled samples to train the classification model. Hence, we propose a novel online batch-based active learning algorithm (OBAL) to perform the labeling. OBAL is developed for crisis management applications where data streams are generated by the social media community. OBAL is applied to discriminate relevant from irrelevant social media items. An emergency management user will be interactively queried to label chosen items. OBAL exploits the boundary items for which it is highly uncertain about their class and makes use of two classifiers: k-Nearest Neighbors (kNN) and Support Vector Machine (SVM). OBAL is equipped with a labeling budget and a set of uncertainty strategies to identify the items for labeling. An extensive analysis is carried out to show OBAL’s performance, the sensitivity of its parameters, and the contribution of the individual uncertainty strategies. Two types of datasets are used: synthetic and social media datasets related to crises. The empirical results illustrate that OBAL has a very good discrimination power.

Abstract: HTTP-based live streaming has become increasingly popular in recent years, and more users have started generating 4K live streams from their devices (e.g., mobile phones) through social-media service providers like Facebook or YouTube. If the audience is located far from a live stream source across the global Internet, TCP throughput becomes substantially suboptimal due to slow start and congestion control mechanisms. This is especially the case when the end-to-end content delivery path involves radio access network at the last mile. As a result, the data rate perceived by a mobile receiver may not meet the high requirement of 4K video streams, which causes deteriorated quality-of-experience (QoE). In this paper, we propose a scheme named edge-based transient holding of live segment (ETHLE), which addresses the above-mentioned issue by performing context-aware transient holding of video segments at the mobile edge with virtualized content caching capability. Through holding the minimum number of live video segments at the mobile edge cache in a context-aware manner, the ETHLE scheme is able to achieve seamless 4K live streaming experiences across the global Internet by eliminating buffering and substantially reducing initial startup delay and live stream latency. It has been deployed as a virtual network function at an LTE-A network, and its performance has been evaluated using real live stream sources that are distributed around the world. The significance of this paper is that by leveraging virtualized caching resources at the mobile edge, we address the conventional transport-layer bottleneck and enable QoE-assured Internet-wide live streaming services with high data rate requirements.

Abstract: Routers in Content-Centric Networking (CCN) may locally cache frequently requested content in order to speed up delivery to end users. Thus, the issue of caching strategies arises, i.e., which content shall be stored and when it should be replaced. In this work, we employ, and study the feasibility of, novel techniques towards intelligent control of CCN routers that autonomously switch between existing caching strategies in response to changing content request patterns. In particular, we present a router architecture for CCN networks that is controlled by rule-based stream reasoning, following the recent formal framework LARS which extends Answer Set Programming for streams. The obtained possibility for flexible router configuration at runtime allows for versatile network control schemes and may help advance the further development of CCN. Moreover, the empirical evaluation of our feasibility study shows that the resulting caching agent may give significant performance gains.

Abstract: Forecasts predict that Internet traffic will continue to grow in the near future. A huge share of this traffic is caused by multimedia streaming. The Quality of Experience (QoE) of such streaming services is an important aspect and in most cases the goal is to maximize the bit rate which -- in some cases -- conflicts with the requirements of both consumers and providers. For example, in mobile environments users may prefer a lower bit rate to come along with their data plan. Likewise, providers aim at minimizing bandwidth usage in order to reduce costs by transmitting less data to users while maintaining a high QoE. Today's adaptive video streaming services try to serve users with the highest bit rates which consequently results in high QoE. In practice, however, some of these high bit rate representations may not differ significantly in terms of perceived video quality compared to lower bit rate representations. In this paper, we present a novel approach to determine the statistically indifferent quality variation (SIQV) of adjacent video representations for adaptive video streaming services by adopting standard objective quality metrics and existing QoE models. In particular, whenever the quality variation between adjacent representations is imperceptible from a statistical point of view, the representation with higher bit rate can be substituted with a lower bit rate representation. As expected, this approach results in savings with respect to bandwidth consumption while still providing a high QoE for users. The approach is evaluated subjectively with a crowdsourcing study. Additionally, we highlight the benefits of our approach, by providing a case study that extrapolates possible savings for providers.

Abstract: Forwarding decisions in classical IP-based networks are predetermined by routing. This is necessary to avoid loops, inhibiting opportunities to implement an adaptive and intelligent forwarding plane. Consequently, content distribution efficiency is reduced due to a lack of inherent multi-path transmission. In Named Data Networking (NDN) instead, routing shall hold a supporting role to forwarding, providing sufficient potential to enhance content dissemination at the forwarding plane. In this paper we design, implement, and evaluate a novel probability-based forwarding strategy, called Stochastic Adaptive Forwarding (SAF) for NDN. SAF imitates a self-adjusting water pipe system, intelligently guiding and distributing Interests through network crossings circumventing link failures and bottlenecks. Just as real pipe systems, SAF employs overpressure valves enabling congested nodes to lower pressure autonomously. Through an implicit feedback mechanism it is ensured that the fraction of the traffic forwarded via congested nodes decreases. By conducting simulations we show that our approach outperforms existing forwarding strategies in terms of the Interest satisfaction ratio in the majority of the evaluated scenarios. This is achieved by extensive utilization of NDN's multipath and content-lookup capabilities without relying on the routing plane. SAF explores the local environment by redirecting requests that are likely to be dropped anyway. This enables SAF to identify new paths to the content origin or to cached replicas, circumventing link failures and resource shortages without relying on routing updates.

Abstract: The emergence of Information-Centric Networking (ICN) provides considerable opportunities for context-aware data distribution in the network's forwarding plane. While packet forwarding in classical IP-based networks is basically predetermined by routing, ICN foresees an adaptive forwarding plane considering the requirements of network applications. As research in this area is still at an early stage, most of the work so far focused on providing the basic functionality, rather than on considering the available context information to improve Quality of Service (QoS). This article investigates to which extent existing forwarding strategies take account of the available context information and can therefore increase service quality. The article examines a typical scenario encompassing different user applications (Voice over IP, video streaming, and classical data transfer) with varying demands (context), and evaluates how well the applications' requirements are met by the existing strategies.

Abstract: Conversational services (e.g., Internet telephony) exhibit hard Quality of Service (QoS) requirements, such as low delay and jitter. Current IP-based solutions for conversational services use push-based data transfer only, since pull-based communication as envisaged in Named Data Networking (NDN) suffers from the two-way delay. Unfortunately, IP's addressing scheme requires additional services for contacting communication partners. NDN provides an inherent solution for this issue by using a location-independent naming scheme. Nevertheless, it currently does not provide a mechanism for push-based data transfer. In this paper, we investigate Persistent Interests as a solution for push-based communication. We improve and implement the idea of Persistent Interests, and study their applicability for conversational services in NDN. This is done by comparing different push- and pull-based approaches for Internet telephony.

Abstract: Persistent Interests (PIs) are a promising approach to introduce push-type traffic in Named Data Networking (NDN), in particular for conversational services such as voice and video calls. Forwarding decisions for PIs are crucial in NDN because they establish a long-lived path for the data flowing back toward the PI issuer. In the course of studying the use of PIs in NDN, we investigate adaptive PI forwarding and present a strategy combining regular NDN forwarding information and results from probing potential alternative paths through the network. Simulation results indicate that our adaptive PI forwarding approach is superior to the PI-adapted Best Route strategy when network conditions change due to link failures.

Abstract: The papers in this special issue focus on the deployment of video over future networks. The past decade has seen how major improvements in broadband and mobile networks have led to widespread popularity of video streaming applications, and how the latter now becomes the major driving force behind exponentially growing Internet traffic. This special issue seeks to investigate these future Internet technologies through the prism of its most prevalent application, that of video communications. video.

Abstract: The computer communication research community shows significant interest in the paradigm of Information-Centric Networking (ICN). Continuously, new proposals for ICN-related challenges (caching, forwarding, etc.) are published. However, due to a lack of a readily available testbed, the majority of these proposals is evaluated either by theoretical analysis and/or by conducting network simulations potentially masking further challenges that are not observable in synthetic environments. Therefore, this article presents a framework for an ICN testbed using low-budget physical hardware with little deployment and maintenance effort for the individual researcher; specifically, Named Data Networking is considered. The employed hardware and software are powerful enough for most research projects, but extremely resource intensive tasks may push both components towards their limits. The testbed framework is based on well established open source software and provides the tools to readily investigate important ICN characteristics on physical hardware emulating arbitrary network topologies. The article discusses the testbed architecture and provides first results obtained from emulations that investigate the performance of various forwarding strategies. The results indicate that further challenges have to be overcome when heading towards a real-world deployment of ICN-based communication.

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.

Abstract: Today, the global share and increase of Internet traffic is largely caused by multimedia delivery, mainly encompassing video, audio and image sharing on social, news, and entertainment platforms. This fact is well known to the Internet research community, which tries to counteract by increasing the content delivery efficiency. So-called Information-Centric Networks (ICN) are of considerable interest, advertised as enablers for intelligent networks, where effective delivery is to be provided as an inherent network feature. Most research proposals in this area are evaluated in simulated environments, using simulation frameworks such as OMNeT++ or ns-3. However, simulations always have shortcomings and cannot substitute measurements in physical networks. In this demonstration, we show how to readily set up an ICN-based testbed using low-budget single-board computers to conduct comprehensive emulations. We choose the scenario of pull-based adaptive video delivery as a showcase and evaluate the performance of different client-based adaptation mechanisms at the application level and different content forwarding strategies at the network level. All of the presented tools and visualization features are provided as open source contributions to the community.