Anticipatory Buffer Control and Quality Selection for Wireless Video Streaming

Video streaming is in high demand by mobile users, as recent studies indicate. In cellular networks, however, the unreliable wireless channel leads to two major problems. Poor channel states degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: buffer underruns occur. In contrast to that, good channel conditions cause common greedy buffering schemes to pile up very long buffers. Such over-buffering wastes expensive wireless channel capacity. To keep buffering in balance, we employ a novel approach. Assuming that we can predict data rates, we plan the quality and download time of the video segments ahead. This anticipatory scheduling avoids buffer underruns by downloading a large number of segments before a channel outage occurs, without wasting wireless capacity by excessive buffering. We formalize this approach as an optimization problem and derive practical heuristics for segmented video streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed measurements show that our solution essentially eliminates playback interruptions without significantly decreasing video quality

Tube streaming: Modelling collaborative media streaming in urban railway networks

We propose a quality assessment framework for crowdsourced media streaming in urban railway networks. We assume that commuters either tune in to some TV/radio channel, or submit requests for content they desire to watch or listen to, which eventually forms a playlist of videos/podcasts/tunes. Given that connectivity is challenged by the movement of trains and the disconnection that this movement causes, users collabo-ratively download (through cellular and WiFi connections) and share content, in order to maintain undisrupted playback. We model collaborative media streaming for the case of the London Underground train network. The proposed quality assessment framework comprises a utility function which characterises the Quality of Experience (QoE) that users (subjectively) perceive and takes into account all the necessary parameters that affect smooth playback. The framework can be used to assess the media streaming quality in any railway network, after adjusting the related parameters. To the best of our knowledge, this is the first study to quantify the perceptual quality of collaborative media streaming in (underground) railway networks from a modelling perspective, as opposed to a systems perspective. Based on real commuter traces from the London Underground network, we evaluate whether audio and video can be streamed to commuters with acceptable QoE. Our results show that even with very high-speed Internet connection, users still experience disruptions, but a carefully designed collaborative mechanism can result in high levels of perceived QoE even in such disruptive scenarios

Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks

Video streaming currently accounts for the majority of Internet traffic. One factor that enables video streaming is HTTP Adaptive Streaming (HAS), that allows the users to stream video using a bit rate that closely matches the available bandwidth from the server to the client. MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a widely used standard, that allows the clients to select the resolution to download based on their own estimations. The algorithm for determining the next segment in a DASH stream is not partof the standard, but it is an important factor in the resulting playback quality. Nowadays vehicles are increasingly equipped with mobile communication devices, and in-vehicle multimedia entertainment systems. In this paper, we evaluate the performance of various DASH adaptation algorithms over a vehicular network. We present detailed simulation results highlighting the advantages and disadvantages of various adaptation algorithms in delivering video content to vehicular users, and we show how the different adaptation algorithms perform in terms of throughput, playback interruption time, and number of interruptions

Flow Level QoE of Video Streaming in Wireless Networks

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for CBR streaming. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of VBR streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior.Comment: 14 page