Video streaming has evolved from push-based, broad-/multicasting approaches with dedicated hard-/software infrastructures to pull-based unicast schemes utilizing existing Web-based infrastructure to allow for better scalability. In this article, we provide an overview of the foundational principles of HTTP Adaptive Streaming (HAS), from video encoding to end user consumption, while focusing on the key advancements in adaptive bitrate algorithms, Quality of Experience (QoE), and energy efficiency. Furthermore, the article highlights the ongoing challenges of optimizing network infrastructure, minimizing latency, and managing the environmental impact of video streaming. Finally, future directions for HAS, including immersive media streaming and neural network-based video codecs, are discussed, positioning HAS at the forefront of next-generation video delivery technologies.

Point clouds (PCs) have attracted researchers and developers due to their ability to provide immersive experiences with six degrees of freedom (6DoF). However, there are still several open issues in understanding the Quality of Experience (QoE) and visual attention of end users while experiencing 6DoF volumetric videos. First, encoding and decoding point clouds require a significant amount of both time and computational resources. Second, QoE prediction models for dynamic point clouds in 6DoF have not yet been developed due to the lack of visual quality databases. Third, visual attention in 6DoF is hardly explored, which impedes research into more sophisticated approaches for adaptive streaming of dynamic point clouds. In this work, we provide an open-source Compressed Point cloud dataset with Eye-tracking and Quality assessment in Mixed Reality (ComPEQ--MR). The dataset comprises four compressed dynamic point clouds processed by Moving Picture Experts Group (MPEG) reference tools (i.e., VPCC and GPCC), each with 12 distortion levels. We also conducted subjective tests to assess the quality of the compressed point clouds with different levels of distortion. The rating scores are attached to ComPEQ--MR so that they can be used to develop QoE prediction models in the context of MR environments. Additionally, eye-tracking data for visual saliency is included in this dataset, which is necessary to predict where people look when watching 3D videos in MR experiences. We collected opinion scores and eye-tracking data from 41 participants, resulting in 2132 responses and 164 visual attention maps in total. The dataset is available at https://ftp.itec.aau.at/datasets/ComPEQ-MR/.

Abstract HTTP Adaptive Streaming (HAS) plays a key role in over-the-top video streaming with the ability to reduce the video stall duration by adapting the quality of transmitted video segments to the network conditions. However, HAS still suffers from two problems. First, it incurs variations in video quality because of throughput fluctuation. Adaptive bitrate (ABR) algorithms at the HAS client usually select a low-quality segment when the throughput drops to avoid stall events, which impairs the Quality of Experience (QoE) of the end-users. Second, many ABR algorithms choose the lowest-quality segments at the beginning of a video streaming session to ramp up the playout buffer early on. Although this strategy decreases the startup time, clients can be annoyed as they have to watch a low-quality video initially. To address these issues, we introduced the H2BR technique (H TTP/2-B ased R etransmission) (Nguyen et al. 33) that utilizes certain features of HTTP/2 (including server push, multiplexing, stream priority, and stream termination) for late transmissions of higher-quality versions of video segments already in the client buffer, in order to improve video quality. Although H2BR was shown to enhance the QoE, limited streaming scenarios were considered resulting in a lack of general conclusions on H2BR’s performance. Thus, this article provides a profound evaluation to answer three open questions: (i) how H2BR’s performance is impacted by parameters at the server side (i.e., various encoding specifications), at the network side (i.e., packet loss rate), and at the client side (i.e., buffer size) on the performance of H2BR; (ii) how H2BR outperforms other state-of-the-art approaches in different configurations of the parameters above; (iii) how to effectively utilize H2BR on top of ABR algorithms in various streaming scenarios. The experimental results show that H2BR’s performance increases with the buffer size and decreases with increasing packet loss rates and/or video segment duration. The number of quality levels can negatively or positively impact on H2BR’s performance, depending on the ABR algorithm deployed. In general, H2BR is able to enhance the video quality by up to 17% and 14% in scalable video streaming and in non-scalable video streaming, respectively. Compared with an existing retransmission technique (i.e., SQUAD Wang et al., ACM Trans Multimed Comput Commun Applic (TOMM) 13(3s): 45, 49), H2BR shows better results with more than 10% in QoE and 9% in the average video quality.

Recent years have witnessed video streaming demands evolve into one of the most popular Internet applications. With the ever-increasing personalized demands for highdefinition and low-latency video streaming services, networkassisted video streaming schemes employing modern networking paradigms have become a promising complementary solution in the HTTP Adaptive Streaming (HAS) context. The emergence of such techniques addresses long-standing challenges of enhancing users’ Quality of Experience (QoE), end-to-end (E2E) latency, as well as network utilization. However, designing a cost-effective, scalable, and flexible network-assisted video streaming architecture that supports the aforementioned requirements for live streaming services is still an open challenge. This article leverages novel networking paradigms, i.e., edge computing and Network Function Virtualization (NFV), and promising video solutions, i.e., HAS, Video Super-Resolution (SR), and Distributed Video Transcoding (TR), to introduce A Latency-and cost-aware hybrId P2P-CDN framework for liVe video strEaming (ALIVE). We first introduce the ALIVE multi-layer architecture and design an action tree that considers all feasible resources (i.e., storage, computation, and bandwidth) provided by peers, edge, and CDN servers for serving peer requests with acceptable latency and quality. We then formulate the problem as a Mixed Integer Linear Programming (MILP) optimization model executed at the edge of the network. To alleviate the optimization model’s high time complexity, we propose a lightweight heuristic, namely, Greedy-Based Algorithm (GBA). Finally, we (i) design and instantiate a large-scale cloud-based testbed including 350 HAS players, (ii) deploy ALIVE on it, and (iii) conduct a series of experiments to evaluate the performance of ALIVE in various scenarios. Experimental results indicate that ALIVE (i) improves the users’ QoE by at least 22%, (ii) decreases incurred cost of the streaming service provider by at least 34%, (iii) shortens clients’ serving latency by at least 40%, (iv) enhances edge server energy consumption by at least 31%, and (v) reduces backhaul bandwidth usage by at least 24% compared to baseline approaches.

HTTP Adaptive Streaming (HAS) solutions utilize various Adaptive BitRate (ABR) algorithms to dynamically select appropriate video representations, aiming at adapting to fluctuations in network bandwidth. However, current ABR implementations have a limitation in that they are designed to function with one set of video representations, i.e., the bitrate ladder, which differ in bitrate and resolution, but are encoded with the same video codec. When multiple codecs are available, current ABR algorithms select one of them prior to the streaming session and stick to it throughout the entire streaming session. Although newer codecs are generally preferred over older ones, their compression efficiencies differ depending on the content’s complexity , which varies over time. Therefore, it is necessary to select the appropriate codec for each video segment to reduce the requested data while delivering the highest possible quality. In this article, we first provide a practical example where we compare compression efficiencies of different codecs on a set of video sequences. Based on this analysis, we formulate the optimization problem of selecting the appropriate codec for each user and video segment (on a per-segment basis in the outmost case), refining the selection of the ABR algorithms by exploiting key metrics, such as the perceived segment quality and size. Subsequently, to address the scalability issues of this centralized model, we introduce a novel distributed plug-in ABR algorithm for Video on Demand (VoD) applications called MEDUSA to be deployed on top of existing ABR algorithms. MEDUSA enhances the user’s Quality of Experience (QoE) by utilizing a multi-objective function that considers the quality and size of video segments when selecting the next representation. Using quality information and segment size from the modified Media Presentation Description (MPD) , MEDUSA utilizes buffer occupancy to prioritize quality or size by assigning specific weights in the objective function. To show the impact of MEDUSA, we compare the proposed plug-in approach on top of state-of-the-art techniques with their original implementations and analyze the results for different network traces, video content, and buffer capacities. According to the experimental findings, MEDUSA shows the ability to improve QoE for various test videos and scenarios. The results reveal an impressive improvement in the QoE score of up to 42% according to the ITU-T P.1203 model (mode 0). Additionally, MEDUSA can reduce the transmitted data volume by up to more than 40% achieving a QoE similar to the techniques compared, reducing the burden on streaming service providers for delivery costs.

5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multime-dia Services (MS). To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs) based on the QoE requirements of different MS services. We then propose SARENA, an _S_FC-enabled ArchitectuRe for adaptive VidEo StreamiNg Applications. Next, we formulate the problem as a central scheduling optimization model executed at the SDN controller. We also present a lightweight heuristic solution consisting of two phases that run on the SDN controller and edge servers to alleviate the time complexity of the optimization model in large-scale scenarios. Finally, we design a large-scale cloud-based testbed including 250 HTTP Adaptive Streaming (HAS) players requesting two popular MS applications (i.e., live and VoD), conduct various experiments, and compare its effectiveness with baseline systems. Experimental results illustrate that SARENA outperforms baseline schemes in terms of users' QoE by at least 39.6%, latency by 29.3%, and network utilization by 30% in both MS services.

The challenging demands for the next generation of the Internet of Things have led to a massive increase in edge computing and network virtualization technologies. One significant technology is Multi-access Edge Computing (MEC), a central piece of 5G telecommunication systems. MEC provides a cloud computing platform at the edge of the radio access network and is particularly essential to satisfy the challenging low-latency demands of future applications. Our previous pub-lications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking (SDN) and virtual IP+port addresses for registered edge services. Building on our already efficient approach, in this work, we propose significant improvements to scale our transparent solution to large-scale real-world access networks. First, by improving the modularity of our SDN controller design, we enable various options to distribute both the SDN controller's load and the switches' flows. Second, we introduce the Unique Mask, a solution superior to the Unique Prefix presented in our previous work that considerably reduces the number of required flows in the switches. Our evaluations show that both algorithms perform very well, with the Unique Mask capable of reducing the number of flows by up to 98 %.

The challenging demands for the next generation of the Internet of Things have led to a massive increase in edge computing and network virtualization technologies. While there is vast potential for research in these areas, managing complex adaptive infrastructure is difficult, and experiments with real hardware are tedious to set up. Furthermore, proposed solutions often require expensive hardware or labor-intensive procedures to replicate and build on these ideas. With our C3-Edge testbed, we address these challenges and propose a novel approach for automated edge testbed setup with a low-cost software-defined network and adaptive infrastructure configuration. We validated the efficiency of our approach on a real-world computing continuum infrastructure. The evaluation results confirm that our flexible approach is suitable for all but the most bandwidth-intensive applications.

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session’s bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE.

Point Cloud (PC) streaming has recently attracted research attention as it has the potential to provide six degrees of freedom (6DoF), which is essential for truly immersive media. PCs require high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Mixed Reality (MR) is a novel technology and has recently become popular. However, quality evaluations of PCs in MR environments are still limited to static images. In this paper, we perform a subjective study on four impact factors on the QoE of PC video sequences in MR conditions, including quality switches, viewing distance, and content characteristics. The experimental results show that these factors significantly impact QoE. The QoE decreases if the sequence switches to lower quality and/or is viewed at a shorter distance, and vice versa. Additionally, the end user might not distinguish the quality differences between two quality levels at a specific viewing distance. Regarding content characteristics, objects with lower contrast seem to provide better quality scores.

3D objects are important components in Mixed Reality (MR) environments as they allow users to inspect and interact with them in a six degrees of freedom (6DoF) system. Point clouds (PCs) and meshes are two common 3D object representations that can be compressed to reduce the delivered data at the cost of quality degradation. In addition, as the end users can move around in 6DoF applications, the viewing distance can vary. Quality assessment is necessary to evaluate the impact of the compressed representation and viewing distance on the Quality of Experience (QoE) of end users. This paper presents a demonstrator for subjective quality assessment of dynamic PC and mesh objects under different conditions in MR environments. Our platform allows conducting subjective tests to evaluate various QoE influence factors, including encoding parameters, quality switching, viewing distance, and content characteristics, with configurable settings for these factors.

While most of the HTTP adaptive streaming (HAS) traffic continues to be video-on-demand (VoD), more users have started generating and delivering live streams with high quality through popular online streaming platforms. Typically, the video contents are generated by streamers and being watched by large audiences which are geographically distributed far away from the streamers locations. The locations of streamers and audiences create a significant challenge in delivering HAS-based live streams with low latency and high quality. Any problem in the delivery paths will result in a reduced viewer experience. In this paper, we propose HxL3, a novel architecture for low-latency live streaming. HxL3 is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches. By holding the minimum number of live media segments through efficient caching and prefetching policies at the edge, improved transmissions, as well as transcoding capabilities, HxL3 is able to achieve high viewer experiences across the Internet by alleviating rebuffering and substantially reducing initial startup delay and live stream latency. HxL3 can be easily deployed and used. Its performance has been evaluated using real live stream sources and entities that are distributed worldwide. Experimental results show the superiority of the proposed architecture and give good insights into how low latency live streaming is working.

The edge computing paradigm brings cloud capabilities close to the clients. Leveraging the edge’s capabilities can improve video streaming services by employing the storage capacity and processing power at the edge for caching and transcoding tasks, respectively, resulting in video streaming services with higher quality and lower latency. In this paper, we propose, a Cost-and Delay-aware Light-weight Transcoding approach at the Edge, in the context of HTTP Adaptive Streaming (HAS). The encoding of a video segment requires computationally intensive search processes. The main idea of is to store the optimal search results as metadata for each bitrate of video segments and reuse it at the edge servers to reduce the required time and computational resources for transcoding. Aiming at minimizing the cost and delay of Video-on-Demand (VoD) services, we formulate the problem of selecting an optimal policy for serving segment requests at the edge server, including (i) storing at the edge server, (ii) transcoding from a higher bitrate at the edge server, and (iii) fetching from the origin or a CDN server, as a Binary Linear Programming (BLP) model. As a result, stores the popular video segments at the edge and serves the unpopular ones by transcoding using metadata or fetching from the origin/CDN server. In this way, in addition to the significant reduction in bandwidth and storage costs, the transcoding time of a requested segment is remarkably decreased by utilizing its corresponding metadata. Moreover, we prove the proposed BLP model is an NP-hard problem and propose two heuristic algorithms to mitigate the time complexity of . We investigate the performance of in comprehensive scenarios with various video contents, encoding software, encoding settings, and available resources at the edge. The experimental results show that our approach (i) reduces the transcoding time by up to 97%, (ii) decreases the streaming cost, including storage, computation, and bandwidth costs, by up to 75%, and (iii) reduces delay by up to 48% compared to approaches.

With the ever-increasing demands for high-definition and low-latency video streaming applications, network-assisted video streaming schemes have become a promising complementary solution in the HTTP Adaptive Streaming (HAS) context to improve users’ Quality of Experience (QoE) as well as network utilization. Edge computing is considered one of the leading networking paradigms for designing such systems by providing video processing and caching close to the end-users. Despite the wide usage of this technology, designing network-assisted HAS architectures that support low-latency and high-quality video streaming, including edge collaboration is still a challenge. To address these issues, this article leverages the Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing paradigms to proposeAcollaboRative edge-Assisted framewoRk for HTTPAdaptive video sTreaming (ARARAT). Aiming at minimizing HAS clients’ serving time and network cost, besides considering available resources and all possible serving actions, we design a multi-layer architecture and formulate the problem as a centralized optimization model executed by the SDN controller. However, to cope with the high time complexity of the centralized model, we introduce three heuristic approaches that produce near-optimal solutions through efficient collaboration between the SDN controller and edge servers. Finally, we implement theARARATframework, conduct our experiments on a large-scale cloud-based testbed including 250 HAS players, and compare its effectiveness with state-of-the-art systems within comprehensive scenarios. The experimental results illustrate that the proposedARARATmethods (${i}$) improve users’ QoE by at least 47%, (ii) decrease the streaming cost, including bandwidth and computational costs, by at least 47%, and (iii) enhance network utilization, by at least 48% compared to state-of-the-art approaches.

Designing a cost-effective, scalable, and flexible architecture that supports low latency and high quality live video streaming is still a challenge for Over-The-Top (OTT) service providers. To cope with this issue, this paper leverages Peer-to-Peer (P2P), Content Delivery Network (CDN), edge computing, Network Function Virtualization (NFV), and distributed video transcoding paradigms to introduce a hybRId P2P-DN arcHfiTecture for livE video stReaming (RICHTER). We first introduce RICHTER's multi-layer architecture and design an action tree that considers all feasible resources provided by peers, edge, and CDN servers for serving peer requests with minimum latency and maximum quality. We then formulate the problem as an optimization model executed at the edge of the network. We present an Online Learning (OL) approach that leverages an unsupervised Self Organizing Map (SOM) to (i) alleviate the time complexity issue of the optimization model and (ii) make it a suitable solution for large-scale scenarios, by enabling decisions for groups of requests instead of for single requests. Finally, we implement the RICHTER framework, conduct our experiments on a large-scale cloud-based testbed including 350 HAS players, and compare its effectiveness with baseline systems. The experimental results illustrate that RICHTER outperforms baseline schemes in terms of users' Quality of Experience (QoE), latency, and network utilization, by at least 59%, 39%, and 70% respectively.

With the emerging demands of high-definition and low-latency video streams, HTTP Adaptive Streaming (HAS) is considered the principal video delivery technology over the Internet. Network-assisted video streaming schemes, which employ modern networking paradigms, e.g., Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing, have been introduced as promising complementary solutions in the HAS context to improve users’ Quality of Experience (QoE) as well as network utilization. However, the existing network-assisted HAS schemes have not fully used edge collaboration techniques and SDN capabilities for achieving the aforementioned aims. To bridge this gap, this paper introduces a coLlaborative Edge- and SDN-Assisted framework for HTTP aDaptive vidEo stReaming (LEADER). In LEADER, the SDN controller collects various information items and runs a central optimization model that minimizes the HAS clients’ serving time, subject to the network’s and edge servers’ resource constraints. Due to the NP-completeness and impractical overheads of the central optimization model, we propose an online distributed lightweight heuristic approach consisting of two phases that runs on the SDN controller and edge servers, respectively. We implement the proposed framework, conduct our experiments on a large-scale testbed including 250 HAS players, and compare its effectiveness with other strategies. The experimental results demonstrate that LEADER outperforms baseline schemes in terms of both users’ QoE and network utilization, by at least 22% and 13%, respectively.

In a 5G testbed, we use 360° video streaming to test, measure, and demonstrate the 5G infrastructure, including the capabilities and challenges of edge computing support. Specifically, we use the SEAWARE (Semantic-Aware View Prediction) software system, originally described in [1], at the edge of the 5G network to support a 360° video player (handling tiled videos) by view prediction. Originally, SEAWARE performs semantic analysis of a 360° video on the media server, by extracting, e.g., important objects and events. This video semantic information is encoded in specific data structures and shared with the client in a DASH streaming framework. Making use of these data structures, the client/player can perform view prediction without in-depth, computationally expensive semantic video analysis. In this paper, the SEAWARE system was ported and adapted to run (partially) on the edge where it can be used to predict views and prefetch predicted segments/tiles in high quality in order to have them available close to the client when requested. The paper gives an overview of the 5G testbed, the overall architecture, and the implementation of SEAWARE at the edge server. Since an important goal of this work is to achieve low motion-to-glass latencies, we developed and describe "tile postloading", a technique that allows non-predicted tiles to be fetched in high quality into a segment already available in the player buffer. The performance of 360° tiled video playback on the 5G infrastructure is evaluated and presented. Current limitations of the 5G network in use and some challenges of DASH-based streaming and of edge-assisted viewport prediction under "real-world" constraints are pointed out; further, the performance benefits of tile postloading are disclosed.

Low latency live streaming over HTTP using Dynamic Adaptive Streaming over HTTP (LL-DASH) and HTTP Live Streaming (LL- HLS) has emerged as a new way to deliver live content with an respectable video quality and short end-to-end latency. Satisfying these requirements while maintaining viewer experience in practice is challenging, and adopting conventional adaptive bitrate (ABR) schemes directly to do so will not work. Therefore, recent solutions including LoL+, L2A, Stallion, and Llama re-think conventional ABR schemes to support low-latency scenarios. These solutions have been integrated with dash.js [9] that supports LL-DASH. However, their performance in LL-HLS remains in question. To bridge this gap, we implement and integrate existing LL-DASH ABR schemes in the hls.js video player [18] which supports LL-HLS. Moreover, a series of real-world trace-driven experiments have been conducted to check their efficiency under various network conditions including a comparison with results achieved for LL-DASH in dash.js. Our version of hls.js is publicly available at [3] and a demo at [4].

Over-the-Top (OTT) service providers need faster, cheaper, and Digital Rights Management (DRM)-capable video streaming solutions. Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, videos are split into short intervals called segments, and each segment is encoded at various qualities/bitrates (i.e., representations) to adapt to the available bandwidth. Utilizing different HAS-based technologies with various segment formats imposes extra cost, complexity, and latency to the video delivery system. Enabling a unified format for transmitting and storing segments at Content Delivery Network (CDN) servers can alleviate the aforementioned issues. To this end, MPEG Common Media Application Format (CMAF) is presented as a standard format for cost-effective and low latency streaming. However, CMAF has not been adopted by video streaming providers yet and it is incompatible with most legacy end-user players. This paper reveals some useful steps for achieving low latency live video streaming that can be implemented for non-DRM sensitive contents before jumping to CMAF technology. We first design and instantiate our testbed in a real OTT provider environment, and then investigate the impact of changing format, segment duration, and Digital Video Recording (DVR) window length on a real live event. The results illustrate that replacing the transport stream (.ts) format with fragmented MP4 (.fMP4) and shortening segments' duration reduces live latency significantly.

Multi-access Edge Computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network that developers can utilize for their applications. In [1] we argued that edge computing should be transparent to clients and introduced a solution to that end. This paper presents how to efficiently implement such a transparent approach, leveraging Software-Defined Networking. For high performance and scalability, our architecture focuses on three aspects: (i) a modular architecture that can easily be distributed onto multiple switches/controllers, (ii) multiple filter stages to avoid screening traffic not intended for the edge, and (iii) several strategies to keep the number of flows low to make the best use of the precious flow table memory in hardware switches. A performance evaluation is shown, with results from a real edge/fog testbed.

Segment prefetching is a technique that transmits the next video segments in advance closer to the user to serve con-tent with reduced latency. Due to its location and capabilities, an edge computing node is an ideal component for executing segment prefetching policies and storing/caching the prefetched segments. In this work, we study segment prefetching techniques deployed at the edge computing node for adaptive video streaming. We propose different types of segment prefetching policies and study their costs and benefits, including segment prefetching based on past segment requests, transrating, a Markov prediction model and machine learning. Besides, we analyze and discuss which segment prefetching policy is better under which circumstances and the influence of the ABR algorithm and the bitrate ladder on segment prefetching.

Recent advances in embedded systems and communication technologies enable novel, non-safety applications in Vehicular Ad Hoc Networks (VANETs). Video streaming has become a popular core service for such applications. In this paper, we present QoCoVi as a QoE- and cost-aware adaptive video streaming approach for the Internet of Vehicles (IoV) to deliver video segments requested by mobile users at specified qualities and deadlines. Considering a multitude of transmission data sources with different capacities and costs, the goal of QoCoVi is to serve the desired video qualities with minimum costs. By applying Dynamic Adaptive Streaming over HTTP (DASH) principles, QoCoVi considers cached video segments on vehicles equipped with storage capacity as the lowest-cost sources for serving requests. We design QoCoVi in two SDN-based operational modes: (i) centralized and (ii) distributed. In centralized mode, we can obtain a suitable solution by introducing a mixed-integer linear programming (MILP) optimization model that can be executed on the SDN controller. However, to cope with the computational overhead of the centralized approach in real IoV scenarios, we propose a fully distributed version of QoCoVi based on the proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM) technique. The effectiveness of the proposed approach is confirmed through emulation with Mininet-WiFi in different scenarios.

Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to$3.7\times$.

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.

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.

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.

Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.

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.

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.

In this work, we present ANGELA, HTTP Adaptive Streaming (HAS) and Edge Computing Simulator. ANGELA was designed to test edge mechanisms that support HAS, as it offers: realistic radio layer simulation, different multimedia content configurations, access to radio and player metrics at the edge, and a wide variety of metrics to evaluate the video streaming session performance. The ANGELA architecture is flexible and can support adaptive bitrate (ABR) algorithms located at different points of the network. Moreover, we show the possibilities of ANGELA by evaluating different ABR algorithms.

Teleoperation of an unmanned aerial vehicle (UAV) is a challenging mobile application with real-time control from a first-person view. It poses stringent latency requirements for both video and control traffic. This paper studies the video quality and latencies for UAV teleoperation over LTE using ns3 simulations. A key ingredient is the latency budget model. We observe that the latency of the video is higher and more sensitive to mobility than that of the control traffic. The latency is influenced by the traffic variation caused by the variable bit rate of the streaming application. High mobility tends to increase latency and lead to more outliers, being problematic in real-time control.

Online games are a fundamental part of the entertainment industry but the current IP infrastructure does not satisfactorily fulfill the needs of these services. The novel networking architecture Named Data Networking (NDN) inherently supports network-level multicast and packet-level security and thereby introduces promising features for online games. In this paper, we propose an NDN-based approach to synchronize game state in a server cluster, a task necessary to allow multiple players in large numbers to play in the same game world. The proposed Quadtree Synchronization Protocol applies NDN’s data-centric nature to decouple the game world from the game servers hosting it. This means that requesting changes of a specific game world region becomes possible without knowing which game server is responsible for the requested region. We use a hierarchic game world structure when requesting data that allows the network to forward requests to the responsible game server without directly addressing it. This region-based naming scheme decouples world regions from servers which eases the management of the game server cluster and allows easier recovery after server failures. In addition, this decoupling allows exchanging information about a geographical region, such as a game world, without knowledge of the other participants changing the world. Such a region-based synchronization mode is not possible to implement with existing protocols. However, it allows building distributed systems that do not require a central server to work. Besides architectural benefits, network emulations show that our protocol increases the efficiency of data transport by utilizing network-level multicast. Our proposed approach can keep up with current protocols which can be used for inter-server game state synchronization.

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.

This research work presents conceptual considerations and quantitative evaluations into how integrating computation offloading to edge computing servers would offer a paradigm shift for an effective deployment of autonomous drones. The specific mission that has been considered is collaborative autonomous navigation and mapping in a 3D environment of a small drone network. Specifically, in order to achieve this mission, each drone is required to compute a low latency, highly compute intensive task in a timely manner. The proposed model decides for each task, while considering the impact on performance and mission requirements, whether to (i) compute locally, (ii) offload to the edge server, or (iii) to the ground station. Extensive simulation work was performed to assess the effectiveness of the proposed scheme compared to other models.

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 periods1, 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 Mulri-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 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.

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: (i) the classic one, with four different media delivery formats, and (ii) 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.

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.

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 extra computational complexity. The emerging software-defined networking (SDN) and network function virtualization (NFV) paradigms enable researchers 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 requesting VRPs over unicast paths. To mitigate the time complexity of the proposed MILP model, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using the MiniNet 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.

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.

The increasing popularity of head-mounted devices and 360° video cameras allows content providers to offer virtual reality video streaming over the Internet, using a relevant representation of the immersive content combined with traditional streaming techniques. While this approach allows the user to freely move her head, her location is fixed by the camera's position within the scene. Recently, an increased interest has been shown for free movement within immersive scenes, referred to as six degrees of freedom. One way to realize this is by capturing objects through a number of cameras positioned in different angles, and creating a point cloud which consists of the location and RGB color of a significant number of points in the three-dimensional space. Although the concept of point clouds has been around for over two decades, it recently received increased attention by ISO/IEC MPEG, issuing a call for proposals for point cloud compression. As a result, dynamic point cloud objects can now be compressed to bit rates in the order of 3 to 55 Mb/s, allowing feasible delivery over today's mobile networks. In this paper, we propose PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple, dynamic point cloud objects. We present a number of rate adaptation heuristics which use information on the user's position and focus, the available bandwidth, and the client's buffer status to decide upon the most appropriate quality representation of each object. Through an extensive evaluation, we discuss the advantages and drawbacks of each solution. We argue that the optimal solution depends on the considered scene and camera path, which opens interesting possibilities for future work.

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 push-like 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.

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.

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.

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 that 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 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.

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.

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.

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.

Adaptive streaming strategies over HTTP allow to serve heterogeneous video users with varying demands. By providing different encoded versions (representations) of each video sequence on the server, clients have the freedom to select a representation that best fits their needs. While the topic of selecting a representation based on a pre-defined set is covered very well in the literature, the problem of how to properly select the representation set stored at the main server is usually an overlooked challenge. In this work, we provide an analysis on how the choice of representations on the server impacts the clients' quality. This is achieved by conducting NS-3 based simulations with a total of 10k users and up to 300 concurrent DASH clients for several recommended sets (e.g., Netflix, YouTube, and Apple), and measuring the experienced quality over a timespan of 24 hours. The results show that under heavy load (at peak hours) there is still room for improvement.

Adaptive content delivery is the state of the art in real-time multimedia streaming. Leading streaming approaches, e.g., MPEG-DASH and Apple HTTP Live Streaming (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.

This paper describes a design framework for TCP-friendly and media-friendly rate control algorithms for multimedia streaming applications. The idea of this framework is to start from a TCP-friendly Rate Control (TFRC) transmission rate and then alter this transmission rate so that it tracks the media characteristics of the stream (e.g., bitrate) or other application characteristics like the client buffer fill level. In this way, the media-friendly property of the algorithm is achieved. We give three rules that guide how the TFRC throughput should track the evolution of the stream's media characteristics and remain TCP-friendly in the long term. We also present, as proof of concept, four simple media-friendly and TCP-friendly congestion control algorithms built using the aforementioned framework. These congestion control algorithms are better suited for multimedia streaming applications than traditional TCP congestion control or smooth congestion control algorithms like TFRC. We have performed evaluations of two of the four proposed media-friendly and TCP-friendly congestion control algorithms under various network conditions and validated that they represent viable transport solutions, better than TFRC, for variable bitrate video streams. More specifically, our two media-friendly and TCP-friendly congestion control algorithms maintained a TCP-friendly throughput in the long term in all experiments and avoided an empty buffer at the client side in situations when TFRC could not achieve this.

With video streaming becoming more and more popular, the number of devices that are capable of streaming videos over the Internet is growing. This leads to a heterogeneous device landscape with varying demands. Dynamic Adaptive Streaming over HTTP (DASH) offers an elegant solution to these demands. Smart adaptation logics are able to adjust the clients' streaming quality according to several (local) parameters. Recent research indicated benefits of blending Scalable Video Coding (SVC) with DASH, especially considering Future Internet architectures. However, except for a DASH dataset with a single SVC encoded video, no other datasets are publicly available. The contribution of this paper is two-fold. First, a DASH/SVC dataset, containing multiple videos at varying bitrates and spatial resolutions including 1080p, is presented. Second, a toolchain for multiplexing SVC encoded videos is provided, therefore making our results reproducible and allowing researchers to generate their own datasets.

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.

This paper presents the usage of CCN, which is a candidate for the next-generation Internet, in combination with the new Dynamic Adaptive Streaming over HTTP (DASH) standard, which was recently ratified by ISO/IEC MPEG. In contrast to the Internet Protocol, which is mainly based on the host-to-host connection paradigm originated in the 1970s, Content Centric Networking (CCN) focuses on the content itself, instead of its location. Considering the dominance of multimedia traffic in todays' Internet, the streaming performance of DASH over CCN as well as the problems introduced by this combination is worth to be investigated in detail. Therefore, we evaluate the protocol overhead introduced by the usage of CCN compared to the HTTP versions 1.0 and 1.1. Furthermore, the performance of DASH over CCN under different network conditions is compared to the performance of HTTP 1.0/1.1. Our results showed that although CCN comes together with higher protocol overhead than HTTP 1.0/1.1 as well as a prototype implementation, it can definitely compete with HTTP 1.0 in media streaming. Based on the evaluation results, problems as well as improvement possibilities are identified, which are the basis for future work in this area.

MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard that has been recently ratified as an international standard (IS). In comparison to other streaming systems, e.g., HTTP progressive download, DASH is able to handle varying bandwidth conditions providing smooth streaming. Furthermore, it enables NAT and Firewall traversal, flexible and scalable deployment as well as reduced infrastructure costs due to the reuse of existing Internet infrastructure components, e.g., proxies, caches, and Content Distribution Networks (CDN). Recently, the Hypertext Transfer Protocol Bis (httpbis) working group of the IETF has officially started the development of HTTP 2.0. Initially three major proposals have been submitted to the IETF i.e., Googles' SPDY, Microsofts' HTTP Speed+Mobility and Network-Friendly HTTP Upgrade, but SPDY has been chosen as working draft for HTTP 2.0. In this paper we implemented MPEG-DASH over HTTP 2.0 (i.e., SPDY), demonstrating its potential benefits and drawbacks. Moreover, several experimental evaluations have been performed that compare HTTP 2.0 with HTTP 1.1 and HTTP 1.0 in the context of DASH. In particular, the protocol overhead, the performance for different round trip times, and DASH with HTTP 2.0 in a lab test scenario has been evaluated in detail.

Social media offers an opportunity for emergency management to identify issues that need immediate reaction. To support the effective use of social media, an analysis approach is needed to identify crisis-related hotspots. We consider in this investigation the analysis of social media (i.e., Twitter, Flickr and YouTube) to support emergency management by identifying sub-events. Sub-events are significant hotspots that are of importance for emergency management tasks. Aiming at sub-event detection, recognition and tracking, the data is processed online in real-time. We introduce an incremental feature selection mechanism to identify meaningful terms and use an online clustering algorithm to uncover sub-events on-the-fly. Initial experiments are based on tweets enriched with Flickr and YouTube data collected during Hurricane Sandy. They show the potential of the proposed approach to monitor sub-events for real-world emergency situations.

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. [1][2] 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 [3], 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 [4] 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.

Scalability within media coding allows for content adaptation towards heterogeneous user contexts and enables in-network adaptation. However, there is no straightforward solution how to encode the content in a scalable way while maximizing rate-distortion performance. In this paper we provide encoding guidelines for scalable video coding based on a survey of media streaming industry solutions and a comprehensive performance evaluation using four state of the art scalable video codecs with a focus on high-definition content (1080p).

Scalable Video Coding (SVC) in media streaming enables dynamic adaptation based on device capabilities and network conditions. In this paper, we investigate deployment options of SVC for Dynamic Adaptive Streaming over HTTP (DASH) with a special focus on scalability options, which are relevant for dynamic adaptation, especially in wireless and mobile environments. We evaluate the performance of SVC with respect to spatial and quality scalability options and compare it to non-scalable Advanced Video Coding (AVC). Performance evaluations are performed for various encoder implementations with high-definition (1080p) content. We show that a hybrid approach with multiple independent SVC bitstreams can have advantages in storage requirements at comparable rate-distortion performance.

The distribution of video content over P2P systems has become a popular and cost-effective option in recent years due to the increasing online availability of content. Additionally, the diversity of end-user terminals used for consuming content demands the provisioning of content in different qualities. Both problems are addressed by distributing layered video content over P2P networks. A piece-picking algorithm for layered video content needs to ensure that the pieces are received in time as well as that the best possible quality that can be processed by the end-user terminal and downloaded under the given network conditions is provided. In this paper, we describe our algorithm for piece-picking of layered video content in Bittorrent-based P2P systems and compare it to other existing piece-picking algorithms. The evaluation presented in this paper shows that our algorithm can very well compete with previously published algorithms.

HTTP streaming has gained significant attraction in the last few years. Currently many commercial as well as standardized streaming systems are already offering adaptive streaming. In most cases, the adaptation is achieved by switching between separately encoded video streams in different qualities. In contrast to that, this paper focuses on the applicability of scalable video coding based on the H.264/SVC standard for adaptive HTTP streaming. Recent work has already highlighted the conceptual advantages like better cache utilization, fine-grained bit rate scalability, and lower storage requirements. This paper discusses the actual realization and design options for implementing priority streaming using the ISO Base Media File Format (BMFF). We propose three different strategies for organizing the scalable video bit stream that consider both the possibilities as well as limitations of the ISO BMFF. The proposed strategies are discussed and evaluated both conceptually and quantitatively. For that purpose, we provide a detailed analysis based on modeling both the overhead of the file format and the HTTP encapsulation. The results for all three priority streaming strategies show that the limitations of the ISO BMFF result in a high relative overhead in the case of low bit rate content. However, when applied to high quality content, priority streaming of H.264/SVC can be implemented at a very low cost. Depending on the number of layers and the offered scalability dimensions, different strategies should be chosen to minimize the overhead. Based on the analytical model and the discussion, this paper provides guidance for selecting the most efficient strategy.

Social media are becoming an important instrument for supporting crisis management, due to their broad acceptance and the intensive usage of mobile devices for accessing them. Social platforms facilitate collaboration among the public during a crisis and also support after-the-fact analysis. Thus, social media are useful for the processes of understanding, learning, and decision making. In particular, having information from social networks in a suitable, ideally summarized, form can speed up such processes. The present study relies on Flickr and You Tube as social media and aims at automatically identifying individual sub-events within a crisis situation. The study applies a two-phase clustering approach to detect those sub-events. The first phase uses geo-referenced data to locate a sub-event, while the second phase uses the natural language descriptions of pictures and videos to further identify the "what-about" of those sub-events. The results show high potential of this social media-based clustering approach for detecting crisis-related sub-events.

This paper describes a toolset for the authoring, simulating, and rendering of multimedia content annotated with Sensory Effect Metadata (SEM) descriptions as specified in Part 3 of the MPEG V standard. This part of MPEG-V standardizes the description of sensory effects (e.g., light, wind) in order to be rendered on sensory devices (e.g., fans, vibration chairs) aiming at generating a sensory experience stimulating possibly all human senses. Our implementation comprises a toolset to author sensory effects associated with multimedia content and the simulation thereof. Furthermore, it includes a library, a standalone player, and a Web browser plug-in which enables the playback and rendering of sensory effects on off-the-shelf rendering devices and in various contexts. All software modules are available under the GNU General Public License (GPL) v3 and the GNU Lesser General Public License (LGPL) v3 respectively.

Universal Multimedia Access (UMA) calls for solutions where content is created once and subsequently adapted to given requirements. With regard to UMA and scalability, which is required often due to a wide variety of end clients, the best suited codecs are wavelet based (like the MC-EZBC) due to their inherent high number of scaling options. However, most transport technologies for delivering videos to end clients are targeted toward the H.264/AVC standard or, if scalability is required, the H.264/SVC. In this paper we will introduce a mapping of the MC-EZBC bitstream to existing H.264/SVC based streaming and scaling protocols. This enables the use of highly scalable wavelet based codecs on the one hand and the utilization of already existing network technologies without accruing high implementation costs on the other hand. Furthermore, we will evaluate different scaling options in order to choose the best option for given requirements. Additionally, we will evaluate different encryption options based on transport and bitstream encryption for use cases where digital rights management is required.

In this paper the performance of layered piece-picking algorithms for Bittorrent-based peer-to-peer systems is evaluated and compared to traditional single layer solutions. In a Bittorrent-based peer-to-peer system, the piece-picking algorithm needs to ensure that the pieces of an audiovisual content are received in time to ensure smooth playback of the content. For layered content provided in multiple qualities, the task of the algorithm becomes more complex, as it has to consider the layer of the pieces to be fetched in addition to the deadline. Thus, the goal of the layered piece-picking algorithm is to ensure that the best possible quality under the given network conditions is received while the playback is not disturbed. To illustrate the advantages of such a layered piece-picking algorithm, its performance is evaluated and compared to a single layer piece-picking algorithm. The results show that layered piece-picking algorithms can significantly improve the quality in terms of peak signal-to-noise ratio.

Today's omnipresent demand for access to multimedia content via diverse devices places new challenges on efficient content delivery. While the Scalable Video Coding (SVC) extension of Advanced Video Coding (AVC) has proven to be a useful tool for the advanced delivery of video content, it has not yet found major adoption in practice. This paper introduces the concept of SVC tunneling developed in the EU FP7 ALICANTE project, which attempts to provide device-independent access to media resources at reduced network load. For SVC tunneling, video transcoding is performed at the ingress/egress points of the network, which may impact the video quality. We show that the quality impact of these transcoding steps for a transcoding chain from MPEG-2 to SVC and back to MPEG-2 accumulates to a PSNR reduction of up to 2.1 dB for transcoding at fixed target bitrates. We also discuss research challenges and open issues in SVC tunneling.

Wireless router platforms based on the Linux operating system are becoming popular in consumers' home networks. The transmission of multimedia data or their use as media-aware network elements imposes high traffic and computational loads on these devices. Thus, it is interesting to evaluate the networking and processing capabilities of such home router platforms in order to assess their usefulness for improved multimedia services such as in-network H.264/SVC video stream adaptation. This paper presents a performance evaluation of three home router platforms representative for low-end, mid-range, and high-end devices. The scope of the evaluation is the performance of the Linux networking stack on these routers; results for both application-layer (TCP and UDP) transmission and kernel-level (UDP) traffic routing are given. The results show that both TCP and UDP throughputs are significantly below (less than half of) the outgoing (wired) links' nominal capacities and depend very much on the sizes of the transmitted data blocks. This clearly indicates that the networking performance is limited by the platforms' processing capabilities and the lack of mechanisms that offload networking tasks from the CPUs. This behaviour cannot be observed on today's PC systems and has to be considered when deploying multimedia services on these network devices. Furthermore, a detailed analysis of the Linux networking stack reveals that the performance is heavily impacted by the netfilter code, even when no packet filtering or network address translation is being performed. Considerable performance gains can be achieved when this netfilter code is bypassed.

Adaptive video streaming based on TCP/HTTP is becoming popular because of its ability to adapt to changing network conditions. We present an in-depth experimental analysis of the use of HTTP-based request-response streams for video streaming. In this scheme, video fragments are fetched by a client from the server, in smaller units called chunks, potentially via multiple parallel HTT P requests (TCP connections). A model for the achievable throughput is formulated.The model is validated by a broad range of streaming experiments, including an evaluation of TCP-friendliness.

In this paper, we present an approach for in-network adaptation of H.264/SVC in the context of 802.11 wireless networks. It builds upon our previous work on an adaptive RTSP/RTP proxy which allows to adapt video streams on Linux-based home router platforms. The proposed approach tackles the throughput variations that occur as a consequence of the physical rate adaptation in 802.11 equipment caused by the mobility of clients. By combining monitoring information available exclusively on the wireless router with the ability to adapt scalable video streams on-the-fly, the proposed in-network adaptation approach allows to quickly adjust the video bit rate to the current link conditions. Instead of reacting on packet loss, our approach uses an increase in queueing delay at the router to detect phases of throughput degradation. This allows a higher responsiveness compared to traditional end-to-end approaches that rely solely on RTCP feedback. The behavior of our novel approach was evaluated in several mobility scenarios in an experimental test bed. The results obtained by streaming and adapting high-definition content clearly demonstrate the feasibility and benefits of this approach.

TCP-based video streaming encounters difficulties in unreliable networks with unanticipated packet loss. In combination with high round trip times, the effective throughput deteriorates rapidly and TCP connection resets or stalls may occur. In this paper, we propose a client-driven video transmission scheme which utilizes multiple HTTP/TCP streams. The scheme is largely insensitive to unanticipated packet loss and thereby reduces throughput fluctuations. Since it is based on HTTP, the scheme can easily be deployed in existing network infrastructures. It fosters scalability on the server side by shifting complexity from the server to the clients. Certain features of request-response schemes allow maintaining fairness, despite of using multiple HTTP streams. Making use of TCP, the scheme inherently adapts to congested network links.

Recent work in TCP video streaming indicates that multimedia streaming via TCP provides satisfactory performance when the achievable TCP throughput is approximately twice the media bit rate. However, these conditions may not be achievable on the Internet, e.g., when the delivery path offers insufficient bandwidth or becomes congested due to competing traffic. Therefore, adaptive streaming for videos over TCP is required and a number of rate-control algorithms for video streaming have been proposed and evaluated in the literature.\\

One of the most active research topics in the field of video signal processing is scalable video coding (SVC). The recently published extension of the H.264/AVC video coding standard introduces scalability features by employing a layered encoding of the video stream. In our work we investigated the usage of this scalable extension of H.264/AVC for in-network multimedia adaptation. We developed an RTSP/RTP-based proxy which exploits the layered encoding of the video and can perform real-time video adaptation on an inexpensive off-the-shelf WiFi router. This is achieved by applying a stateful, packet-based adaptation approach that keeps the computational costs at a minimum. With that approach it is possible to simultaneously adapt multiple video streams to varying network conditions or to the capabilities of the consumers' end-devices. In our demonstration we show the streaming of two scalable video streams from a server to a client and the in-network adaptation of the video at the WiFi router. The adaptation can be controlled interactively in the temporal, spatial and SNR domains.

Current trends in smart homes suggest that several multimedia services will soon converge towards common standards and platforms. However this rapid evolution gives rise to several issues related to the management of a large number of multimedia streams in the home communication infrastructure. An issue of particular relevance is how a context acquisition system can be used to support the management of such a large number of streams with respect to the Quality of Service (QoS), to their adaptation to the available bandwidth or to the capacity of the involved devices, and to their migration and adaptation driven by the users’ needs that are implicitly or explicitly notified to the system. Under this scenario this paper describes the experience of the INTERMEDIA project in the exploitation of context information to support QoS, migration, and adaptation of multimedia streams.

In this paper we present an interoperable multimedia delivery framework for scalable video coding based on MPEG-21 digital item adaptation (DIA). In can be used to transmit scalable video contents within heterogeneous usage environments where the properties of the usage environment (e.g., terminal/network capabilities) may change dynamically during the streaming session. The usage environment is signaled by interoperable description formats provided by the DIA standard. Additionally, the adaptation itself is done by exploiting the standardpsilas generic adaptation approach, i.e., independent of the actual coding format. Thus, the overall framework is also applicable for other scalable coding formats.

Recent advances in video coding technology like the scalable extension of the MPEG-4 AVC/H.264 video coding standard pave the way for computationally cheap adaptation of video content. In this paper we present our work on a lightweight RTSP/RTP proxy that enables in-network stream processing. Based on an off-the-shelf wireless router that runs a Linux-based firmware we demonstrate that the video adaptation can be performed on-the-fly directly on a network device. The paper covers design and implementation details of the proxy as well as a discussion about the actual adaptation of the SVC stream. Based on experimental evaluations we show that our approach can handle a reasonable number of concurrent sessions for a typical home deployment scenario. Furthermore, the paper covers possible applications in which adaptation on the network device can be beneficial.

Enabling transparent and augmented use of multimedia content across a wide range of networks and devices is still a challenging task within the multimedia research community. Within multimedia frameworks, content adaptation is the core concept to overcome this issue. Most media adaptation engines targeting Universal Multimedia Access (UMA) scale the content w.r.t. terminal capabilities and network resource constraints and do not sufficiently consider user preferences. This paper focuses on a hybrid recommender technique for configuring a cross-modal utility model that guides adaptation of multimedia content. This approach additionally considers the user environment as well as demographic user data which leads to a personalized and increased multimedia experience. Based on a related adaptation decision technique we show how it is possible to offer a personalized adaptation for the individual user. We present a detailed evaluation of the approach based on results earned by subjective tests.

Due to the increasing heterogeneity in the current multimedia landscape, the delivery of multimedia content has become an important issue today. This heterogeneity is not only reflected by a plethora of different usage environments, but also by the presence of multiple (scalable) coding formats. Therefore, format-independent adaptation engines have to be used within a multimedia delivery platform, which are able to adapt the multimedia content according to a certain usage environment, independent of the underlying coding format of the content. By relying on automatically created textual descriptions of the high-level syntax of binary media resources, a format-independent adaptation engine can be built. MPEG-21 generic bitstream syntax schema (gBS schema) is a tool that is part of the MPEG-21 multimedia framework. It enables the use of generic bitstream syntax descriptions (gBSDs), i.e., textual descriptions in XML, to steer the adaptation of a binary media resource, using format-independent adaptation logic. In this paper, we address the design and performance evaluation of a multimedia delivery platform that relies on gBS schema-driven adaptation engines. Our platform is called MuMiVA; it is a fully integrated, extensible platform for multimedia delivery in heterogeneous usage environments, using streaming technologies. To demonstrate the flexibility of our multimedia delivery platform, we discuss the functioning of two different applications (i.e., exploitation of temporal scalability and shot selection) applied to two different coding formats (i.e., MPEG-4 visual and H.264/AVC).

Universal plug and play (UPnP) is a widely accepted standard for automatically detecting devices and services in a local area network as well as for describing and controlling them. In order to deal with multimedia devices and especially content, in 2002 the UPnP-AV standard definition was released. It defines device and service descriptions for media servers and renderers. Thereby, the media server's content directory service allows an easy management and the exchange of metadata about the provided media data. Media content became browsable by semantic meta information about it. There are still two major drawbacks of UPnP-AV, which make its usage in real world multimedia communication scenarios very difficult. First, searching for similar content on distributed media servers with a huge number of media files is not economically possible. Second, the media content must be consumed by Renderers as provided by the Servers, independently of their terminal capabilities and network connections. In order to deal with these two drawbacks, this work proposes a novel approach of metadata integration and media transcoding in UPnP networks. First, the Media Server is extended by a control point which offers discovery of other media servers and fetches metadata from their content directories. Furthermore, it integrates the gathered information in its own content directory. Control Points are then able to query this Integrating Media Server for a desired content, and get a network-complete search result. Second, terminal and network capabilities of the Renderers are taken into account in order to transcode and transmit the content in a suitable way for the consuming device. These two approaches of metadata integration and media data adaptation enable searchable logical views on tailored multimedia content in UPnP-AV networks.

Providing transparent and augmented use of multimedia content across a wide range of networks and devices is still a challenging task within the multimedia research community. Multimedia adaptation was figured out as a core concept to overcome this issue. Most multimedia adaptation engines for providing Universal Multimedia Access (UMA) scale the content under consideration of terminal capabilities and resource constraints but do not really consider individual user preferences. This paper introduces an adaptive multimedia framework which offers the user a personalized content variation for satisfying his/her individual utility preferences.

Content adaptation is an important issue of multimedia frameworks in order to achieve universal multimedia access (UMA), i.e., to enable consumption of multimedia content independently of the given resource limitations, terminal capabilities, and user preferences. The digital item adaptation (DIA) standard, one of the core specifications of the MPEG-21 framework, supports content adaptation considering a wide range of networks, devices, and user preferences. Most adaptive multimedia frameworks targeting the UMA vision do not consider utility aspects in their adaptation decisions. This paper focuses on a generic semantic-based audio-visual utility model for DIA that aims to enhance the multimedia experience for the user. Our proposed model is able to take the semantics and the perceptual features of the content as well as the users' specific utility aspects into account. Based on a detailed analysis of these constraints, we will show how the model reacts on individual input data. For choosing the best adaptation decision considering resource limitations on client and server sides as well as network characteristics, we evaluate four algorithms for performing this adaptation decision taking task. We will discuss results according to some use case scenarios

In order to enable transparent and convenient use of multimedia content across a wide range of networks and devices, content adaptation is an important issue within multimedia frameworks. The so called digital item adaptation (DIA) standard is one of the core concepts of the MPEG-21 framework that will support the adaptation of multimedia resources according to device capabilities, underlying network characteristics, and user preferences. Most multimedia adaptation engines for providing universal multimedia access (UMA) scale the content with respect to terminal capabilities and resource constraints. This paper focuses on the cross-modal adaptation decision taking process considering the user environment and terminal capabilities as well as resource limitations on the server, network, and client side. This approach represents a step toward increased universal multimedia experience (UME). Based on four different algorithms for solving this optimization process, we present an evaluation of results gained by running their implementations on different test networks

The diversity of end-terminal and access network capabilities as well as the dynamic nature of wireless connections pose significant challenges to providers of multimedia streaming services. In this paper, we present a system based on MPEG-21 Digital Item Adaptation (DIA) technologies that automatically adapts scalable multimedia resources, like upcoming MPEG-4 Scalable Video Coding (SVC) streams, in a generic and transparent way to the user and session context. This context includes terminal and network capabilities as well as user characteristics. A server side adaptation engine reacts to context changes by dynamic decision taking and accordingly modified bitstream adaptation. Furthermore, novel concepts are presented that facilitate multimedia adaptation in a distributed fashion along the delivery path.

XML-based metadata for digital media is becoming increasingly important, as a consequence also calling for efficient encoding and compression schemes for the storage and transport of this metadata. Moreover, support for streaming the XML metadata in conjunction with the media data is highly desirable. Such support is provided, for instance, by MPEG's binary format for metadata (BiM) encoding approach, which facilitates fragmenting, delivering, and accessing the metadata in so-called access units (AUs). In this paper, we present a quantitative evaluation of existing XML metadata compression and encoding techniques, reaching from widely used state-of-the-art data compression algorithms to sophisticated XML-aware encoding schemes. The comparison is based on compressing MPEG-21 generic bitstream syntax descriptions (gBSDs) which can grow to non-negligible sizes. The main conclusion from this investigation is that in terms of pure compression efficiency on XML files, the BiM approach (exemplified by the MPEG reference software as well as a commercial version thereof) is comparable - in terms of performance - with traditional data or specific XML compression tools. However, when XML metadata have to be fragmented, compressed, and streamed in such fragments, the results indicate that the BiM approach is superior to the other schemes.

Video streaming systems in best effort networks have to somehow cope with dynamically changing bandwidth. Various scalable video codecs allow intra-stream adaptation by use of temporal, spatial, or quality (SNR) scalability; optimizations for finer grained scalability are available as layered coding and FGS techniques. However, if there is no scalable video stream at hand, stream switching among pre-encoded stream versions of different bitrates and qualities allows at least coarse-grained adaptation. Those different approaches compete to be the most efficient solution for adaptive video streaming. However, this paper will show that the efficacy is significantly increased by combining those approaches. As will be discussed, the combination of coarse-grained stream switching and temporal intra-stream adaptation offers better visual results and more stable client buffer behavior than the denoted approaches used separately

The seamless access to rich multimedia content on any device and over any network, usually known as Universal Multimedia Access, requires interoperable description tools and adaptation techniques to be developed. To address the latter issue, MPEG-21 Digital Item Adaptation (DIA) introduces the Bitstream Syntax Description (BSD) framework, which provides tools for adapting multimedia content in a generic (i.e., coding format independent) way. The basic idea is to use the eXtensible Markup Language (XML) to describe the high-level structure of a binary media bitstream, to transform its description [e.g., by means of eXtensible Stylesheet Language Transformations (XSLT)], and to construct the adapted media bitstream from the transformed description. This paper presents how this basic BSD framework, initially developed for nonstreamed content and suffering from inherent limitations and high memory consumption of XML-related technologies such as XSLT, can be advanced and efficiently implemented in a streaming environment and on resource-constrained devices. Two different attempts to solve the inherent problems are described. The first approach proposes an architecture based on the streamed processing of Simple Application Programming Interface for XML (SAX) events and adopts Streaming Transformations for XML (STX) as an alternative to XSLT, whereas the second approach breaks a BSD up into well-formed fragments called process units that can be processed individually by a standard XSLT processor. The current status of our work, as well as directions for future research, are given.

Modern video streaming servers should adapt, and switch quality levels of, the streamed data according to precise and timely feedback about the network conditions, and should also incorporate selective retransmissions of important reference frames (I- and P-VOPs). This work evaluates two recent IETF Internet drafts on RTP (real time transport protocol) extensions for immediate feedback and retransmission and shows, in conjunction with temporal video adaptation, how a substantial visual quality gain can be achieved by using those extensions (up to 4.4 dB PSNR under lossy conditions).

If videos are streamed over heterogeneous networks like the Internet, severe bandwidth fluctuations can emerge which hamper the seamless transmission to the end user. To overcome this issue, a video's quality and, as a consequence, its bandwidth requirements can be reduced. Quality reduction in the temporal domain (i.e., frame dropping) turned out to be a promising approach because it is fast and easy to perform. In this paper, we present different approaches for temporal video adaptation and we investigate their performance in terms of the achieved visual quality when applied on several videos. The results show that our QCTVA approach (quality controlled temporal video adaptation), based on PSNR evaluation of frames, yields superior quality.

Multimedia streaming is becoming ever more popular. However, the Internet does not support streaming with its high bandwidth and low latency requirements very well. The problem is that QoS guarantees cannot be given. Hence, communication partners have to deal with rapidly changing connection parameters. This requires sophisticated streaming concepts that can handle these varying conditions using adaptation techniques. Adaptation methods can be dropping layers, dropping access units or transcoding the contents. But this places specific requirements on the underlying protocol. This paper identifies and discusses these requirements and analyses how existing protocols can meet them. Unfortunately, none of the known protocols can meet all requirements. Hence, we propose a new adaptation-aware multimedia streaming protocol that can operate as required in the given Internet environment. Furthermore, we show how this protocol can be used to carry MPEG-4 audio-visual contents.

Due to the increasing availability and use of digital video data on the Web, video caching will be an important performance factor in the future WWW. We propose an architecture of a video proxy cache that integrates modern multimedia and communication stan-dards. Especially we describe features of the MPEG-4 and MPEG-7 multimedia standards that can be helpful for a video proxy cache. QBIX1 supports real-time adaptation in the compressed and in the decompressed domain. It uses adaptation to improve the cache replacement strategies in the proxy, but also to realize media gate-way functionality driven by the clients ’ terminal capabilities.

Multimedia streaming is becoming increasingly popular. The multimedia standard MPEG-4 was designed to support scenes of different levels of complexity and applications with low bandwidth up to very high bandwidth requirements. Scalable video encoding is supported as well. This allows MPEG-4 streaming software to adapt video quality dynamically in the network to currently given QoS conditions. We evaluate packetization modes designed to transport MPEG-4 elementary streams over RTP connections, based on an implementation of an MPEG-4 video server and a demo client. Suitability of the packetization modes for video stream adaptation in the network nodes is discussed.

No abstract available.

Workstation and PC clusters interconnected by SCI (scalable coherent interface) are very promising technologies for high-performance cluster computing. Using commercial SBus to SCI interface cards and system software and drivers, a two-workstation cluster has been constructed for initial testing and evaluation. The PVM system has been adapted to operate on this cluster using both raw channel and shared-memory access to the SCI interconnect, and preliminary communications performance tests have been carried out. To achieve mutual exclusion in accessing shared-memory segments, two protocols were used. Our preliminary results indicate that communications throughput in the range of 17.7 Mbytes/s, and round-trip latencies of 80 μs using the first and 140 μs using the second protocol, can be obtained on SCI clusters. These figures are significantly better (by a factor of 2 to 4) for small and large messages than those attainable on Fast Ethernet LANs. Since these performance results are very encouraging, we expect that, in the very near future, SCI networks will be capable of delivering several tens of Mbytes/s bandwidth and a few tens of microseconds latencies, and will significantly enhance the viability of cluster computing. Copyright © 1999 John Wiley & Sons, Ltd.

Distributed shared objects are a well known approach to achieve independence of the memory model for parallel programming. The illusion of shared (global) objects is a convenient abstraction which leads to ease of programming on both kinds of parallel architectures, shared memory and distributed memory machines. We present several different implementation variants for distributed shared objects on distributed platforms. We have considered these variants while implementing a high level parallel programming model known as coordinators (J. Knopp, 1996). These are global objects coordinating accesses to the encapsulated data according to statically defined access patterns. Coordinators have been implemented on both shared memory multiprocessors and networks of workstations (NOWs). We describe their implementation as distributed shared objects and give basic performance results on a NOW.

This paper addresses the problem of providing high-performance disk I/O in massively parallel computers. Resolving the fundamental I/O bottleneck in parallel architectures involves both hardware and software issues. We review previous work on disk arrays and I/O architectures aimed at providing highly parallel disk I/O subsystems. We then focus on the requirements and design of parallel file systems (PFSs) which are responsible to make the parallelism offered by the hardware and a declustered file organization available to application programs. We present the design strategy and key concepts of a general-purpose file system for a parallel computer with scalable distributed shared memory. The principal objectives of the PFS are to fully exploit the parallelism inherent among and within file accesses, and to provide scalable I/O performance. The machine model underlying the design is described, with an emphasis on the innovative architectural features supporting scalability of the shared memory. Starting from a classification of various scenarios of concurrent I/O requests, the features of the PFS design essential for achieving the goals are described and justified. It is argued that the inter- and intra-request parallelism of the I/O load can indeed be effectively exploited and supported by the parallel system resources. Scalability of I/O performance and of the PFS software can be ensured by avoiding serial bottlenecks through the use of the powerful architectural features.