QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Recently, HTTP-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to network conditions in order to ensure a high quality of experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is designed to operate with a transport latency of few seconds. To reach this goal, LOLYPOP leverages TCP throughput predictions on multiple time scales, from 1 to 10 seconds, along with an estimate of the prediction error distribution. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the quality of experience by maximizing the average video quality as a function of the number of skipped segments and quality transitions. In order to select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm from the literature, called FESTIVE. We observed that the average video quality is by up to a factor of 3 higher than with FESTIVE. We also observed that LOLYPOP is able to reach a broader region in the quality of experience space, and thus it is better adjustable to the user profile or service provider requirements.Comment: Technical Report TKN-16-001, Telecommunication Networks Group, Technische Universitaet Berlin. This TR updated TR TKN-15-00

Adaptive Streaming in P2P Live Video Systems: A Distributed Rate Control Approach

Dynamic Adaptive Streaming over HTTP (DASH) is a recently proposed standard that offers different versions of the same media content to adapt the delivery process over the Internet to dynamic bandwidth fluctuations and different user device capabilities. The peer-to-peer (P2P) paradigm for video streaming allows to leverage the cooperation among peers, guaranteeing to serve every video request with increased scalability and reduced cost. We propose to combine these two approaches in a P2P-DASH architecture, exploiting the potentiality of both. The new platform is made of several swarms, and a different DASH representation is streamed within each of them; unlike client-server DASH architectures, where each client autonomously selects which version to download according to current network conditions and to its device resources, we put forth a new rate control strategy implemented at peer site to maintain a good viewing quality to the local user and to simultaneously guarantee the successful operation of the P2P swarms. The effectiveness of the solution is demonstrated through simulation and it indicates that the P2P-DASH platform is able to warrant its users a very good performance, much more satisfying than in a conventional P2P environment where DASH is not employed. Through a comparison with a reference DASH system modeled via the Integer Linear Programming (ILP) approach, the new system is shown to outperform such reference architecture. To further validate the proposal, both in terms of robustness and scalability, system behavior is investigated in the critical condition of a flash crowd, showing that the strong upsurge of new users can be successfully revealed and gradually accommodated.Comment: 12 pages, 17 figures, this work has been submitted to the IEEE journal on selected Area in Communication

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