Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Network-aware video streaming for future media Internet

272 p

SAP: Stall-aware pacing for improved DASH video experience in cellular networks

The dramatic growth of cellular video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent system complexities that degrade streaming performance, such as variability in both video bitrate and network conditions. In this paper, we present SAP as a DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. SAP achieves this by leveraging both network and client state information to optimize the pacing of individual video flows. We extensively evaluate SAP performance using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparison. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric

EdgeDASH: Exploiting Network-Assisted Adaptive Video Streaming for Edge Caching

While edge video caching has great potential to decrease the core network traffic as well as the users' experienced latency, it is often challenging to exploit the caches in current client-driven video streaming solutions due to two key reasons. First, even those clients interested in the same content might request different quality levels as a video content is encoded into multiple qualities to match a wide range of network conditions and device capabilities. Second, the clients, who select the quality of the next chunk to request, are unaware of the cached content at the network edge. Hence, it becomes imperative to develop network-side solutions to exploit caching. This can also mitigate some performance issues, in particular for the scenarios in which multiple video clients compete for some bottleneck capacity. In this paper, we propose a network-side control logic running at a WiFi AP to facilitate the use of cached video content. In particular, an AP can assign a client station a different video quality than its request, in case the alternative quality provides a better utility. We formulate the quality assignment problem as an optimization problem and develop several heuristics with polynomial complexity. Compared to the baseline where the clients determine the quality adaptation, our proposals, referred to as EdgeDASH, offer higher video quality, higher cache hits, and lower stalling ratio which are essential for user's satisfaction. Our simulations show that EdgeDASH facilitates significant cache hits and decreases the buffer stalls only by changing the client's request by one quality level. Moreover, from our analysis, we conclude that the network assistance provides significant performance improvement, especially when the clients with identical interests compete for a bottleneck link's capacity