Minimizing the impact of delay on live SVC-based HTTP adaptive streaming services

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming services. Video content is temporally split into segments which are offered at multiple qualities to the clients. These clients autonomously select the quality layer matching the current state of the network through a quality selection heuristic. Recently, academia and industry have begun evaluating the feasibility of adopting layered video coding for HAS. Instead of downloading one file for a certain quality level, scalable video streaming requires downloading several interdependent layers to obtain the same quality. This implies that the base layer is always downloaded and is available for playout, even when throughput fluctuates and enhancement layers can not be downloaded in time. This layered video approach can help in providing better service quality assurance for video streaming. However, adopting scalable video coding for HAS also leads to other issues, since requesting multiple files over HTTP leads to an increased impact of the end-to-end delay and thus on the service provided to the client. This is even worse in a Live TV scenario where the drift on the live signal should be minimized, requiring smaller segment and buffer sizes. In this paper, we characterize the impact of delay on several measurement-based heuristics. Furthermore, we propose several ways to overcome the end-to-end delay issues, such as parallel and pipelined downloading of segment layers, to provide a higher quality for the video service

On the merits of SVC-based HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the dominant type of video streaming in Over-The-Top multimedia services. HAS content is temporally segmented and each segment is offered in different video qualities to the client. It enables a video client to dynamically adapt the consumed video quality to match with the capabilities of the network and/or the client's device. As such, the use of HAS allows a service provider to offer video streaming over heterogeneous networks and to heterogeneous devices. Traditionally, the H. 264/AVC video codec is used for encoding the HAS content: for each offered video quality, a separate AVC video file is encoded. Obviously, this leads to a considerable storage redundancy at the video server as each video is available in a multitude of qualities. The recent Scalable Video Codec (SVC) extension of H. 264/AVC allows encoding a video into different quality layers: by dowloading one or more additional layers, the video quality can be improved. While this leads to an immediate reduction of required storage at the video server, the impact of using SVC-based HAS on the network and perceived quality by the user are less obvious. In this article, we characterize the performance of AVC- and SVC-based HAS in terms of perceived video quality, network load and client characteristics, with the goal of identifying advantages and disadvantages of both options

Joint in-network video rate adaptation and measurement-based admission control: algorithm design and evaluation

The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other's performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider's objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider's policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario

Design and evaluation of a DASH-compliant second screen video player for live events in mobile scenarios

The huge diffusion of mobile devices is rapidly changing the way multimedia content is consumed. Mobile devices are often used as a second screen, providing complementary information on the content shown on the primary screen, as different camera angles in case of a sport event. The introduction of multiple camera angles poses many challenges with respect to guaranteeing a high Quality of Experience to the end user, especially when the live aspect, different devices and highly variable network conditions typical of mobile environments come into play. Due to the ability of HTTP Adaptive Streaming (HAS) protocols to dynamically adapt to bandwidth fluctuations, they are especially suited for the delivery of multimedia content in mobile environments. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Recently, a standardized solution has been proposed by the MPEG consortium, called Dynamic Adaptive Streaming over HTTP (DASH). We present in this paper a DASH-compliant iOS video player designed to support research on rate adaptation heuristics for live second screen scenarios in mobile environments. The video player allows to monitor the battery consumption and CPU usage of the mobile device and to provide this information to the heuristic. Live and Video-on-Demand streaming scenarios and real-time multi-video switching are supported as well. Quantitative results based on real 3G traces are reported on how the developed prototype has been used to benchmark two existing heuristics and to analyse the main aspects affecting battery lifetime in mobile video streaming

Random Linear Network Coding for Wireless Layered Video Broadcast: General Design Methods for Adaptive Feedback-free Transmission

This paper studies the problem of broadcasting layered video streams over heterogeneous single-hop wireless networks using feedback-free random linear network coding (RLNC). We combine RLNC with unequal error protection (UEP) and our main purpose is twofold. First, to systematically investigate the benefits of UEP+RLNC layered approach in servicing users with different reception capabilities. Second, to study the effect of not using feedback, by comparing feedback-free schemes with idealistic full-feedback schemes. To these ends, we study `expected percentage of decoded frames' as a key content-independent performance metric and propose a general framework for calculation of this metric, which can highlight the effect of key system, video and channel parameters. We study the effect of number of layers and propose a scheme that selects the optimum number of layers adaptively to achieve the highest performance. Assessing the proposed schemes with real H.264 test streams, the trade-offs among the users' performances are discussed and the gain of adaptive selection of number of layers to improve the trade-offs is shown. Furthermore, it is observed that the performance gap between the proposed feedback-free scheme and the idealistic scheme is very small and the adaptive selection of number of video layers further closes the gap.Comment: 15 pages, 12 figures, 3 tables, Under 2nd round of review, IEEE Transactions on Communication

Design and evaluation of tile selection algorithms for tiled HTTP adaptive streaming (Best paper award)

The future of digital video is envisioned to have an increase in both resolution and interactivity. New resolutions like 8k UHDTV are up to 16 times as big in number of pixels compared to current HD video. Interactivity includes the possibility to zoom and pan around in video. We examine Tiled HTTP Adaptive Streaming (TAS) as a technique for supporting these trends and allowing them to be implemented on conventional Internet infrastructure. In this article, we propose three tile selection algorithms, for different use cases (e.g., zooming, panning). A performance evaluation of these algorithms on a TAS testbed, shows that they lead to better bandwidth utilization, higher static Region of Interest (ROI) video quality and higher video quality while manipulating the ROI. We show that we can transmit video at resolutions up to four times larger than existing algorithms during bandwidth drops, which results in a higher quality viewing experience. We can also increase the video quality by up to 40 percent in interactive video, during panning or zooming

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction

Increasingly, commercial content providers (CPs) offer streaming solutions using peer-to-peer (P2P) architectures, which promises significant scalabil- ity by leveraging clients’ upstream capacity. A major limitation of P2P live streaming is that playout rates are constrained by clients’ upstream capac- ities – typically much lower than downstream capacities – which limit the quality of the delivered stream. To leverage P2P architectures without sacri- ficing quality, CPs must commit additional resources to complement clients’ resources. In this work, we propose a cloud-based service AngelCast that enables CPs to complement P2P streaming. By subscribing to AngelCast, a CP is able to deploy extra resources (angel), on-demand from the cloud, to maintain a desirable stream quality. Angels do not download the whole stream, nor are they in possession of it. Rather, angels only relay the minimal fraction of the stream necessary to achieve the desired quality. We provide a lower bound on the minimum angel capacity needed to maintain a desired client bit-rate, and develop a fluid model construction to achieve it. Realizing the limitations of the fluid model construction, we design a practical multi- tree construction that captures the spirit of the optimal construction, and avoids its limitations. We present a prototype implementation of AngelCast, along with experimental results confirming the feasibility of our service.Supported in part by NSF awards #0720604, #0735974, #0820138, #0952145, #1012798 #1012798 #1430145 #1414119. (0720604 - NSF; 0735974 - NSF; 0820138 - NSF; 0952145 - NSF; 1012798 - NSF; 1430145 - NSF; 1414119 - NSF