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

Analysis of Buffer Starvation with Application to Objective QoE Optimization of Streaming Services

Our purpose in this paper is to characterize buffer starvations for streaming services. The buffer is modeled as an M/M/1 queue, plus the consideration of bursty arrivals. When the buffer is empty, the service restarts after a certain amount of packets are \emph{prefetched}. With this goal, we propose two approaches to obtain the \emph{exact distribution} of the number of buffer starvations, one of which is based on \emph{Ballot theorem}, and the other uses recursive equations. The Ballot theorem approach gives an explicit result. We extend this approach to the scenario with a constant playback rate using T\`{a}kacs Ballot theorem. The recursive approach, though not offering an explicit result, can obtain the distribution of starvations with non-independent and identically distributed (i.i.d.) arrival process in which an ON/OFF bursty arrival process is considered in this work. We further compute the starvation probability as a function of the amount of prefetched packets for a large number of files via a fluid analysis. Among many potential applications of starvation analysis, we show how to apply it to optimize the objective quality of experience (QoE) of media streaming, by exploiting the tradeoff between startup/rebuffering delay and starvations.Comment: 9 pages, 7 figures; IEEE Infocom 201

Subjective quality assessment of longer duration video sequences delivered over HTTP adaptive streaming to tablet devices

HTTP adaptive streaming facilitates video streaming to mobile devices connected through heterogeneous networks without the need for a dedicated streaming infrastructure. By splitting different encoded versions of the same video into small segments, clients can continuously decide which segments to download based on available network resources and device characteristics. These encoded versions can, for example, differ in terms of bitrate and spatial or temporal resolution. However, as a result of dynamically selecting video segments, perceived video quality can fluctuate during playback which will impact end-users' quality of experience. Subjective studies have already been conducted to assess the influence of video delivery using HTTP Adaptive Streaming to mobile devices. Nevertheless, existing studies are limited to the evaluation of short video sequences in controlled environments. Research has already shown that video duration and assessment environment influence quality perception. Therefore, in this article, we go beyond the traditional ways for subjective quality evaluation by conducting novel experiments on tablet devices in more ecologically valid testing environments using longer duration video sequences. As such, we want to mimic realistic viewing behavior as much as possible. Our results show that both video content and the range of quality switches significantly influence end-users' rating behavior. In general, quality level switches are only perceived in high motion sequences or in case switching occurs between high and low quality video segments. Moreover, we also found that video stallings should be avoided during playback at all times