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Characterizing QoE in Large-Scale Live Streaming

机译:在大型直播中表征QoE

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Understanding the impact of performance degradation on users' QoE during live Internet streaming is key to maximize the audience and increase content providers' revenues. It is known that some problems have a strong correlation with low QoE--e.g., users experiencing video stalls tend to leave video sessions earlier. It is, however, mostly unknown whether such observations hold for live streaming of large-scale events (e.g., the FIFA World Cup). Such events are particular due to the widespread interest in the streamed content, reaching an impressively high audience worldwide. We study whether and to what extent performance degradation during live streaming of large-scale events affects users' QoE. We leverage a unique dataset collected from a major content provider in South America during the 2014 FIFA Soccer World Cup. We first extract performance metrics from the logs: stream bitrate, bitrate switches, playback stalls, and playback startup latency. We then correlate these performance metrics with session duration, which we use as a QoE indicator. We confirm the strong correlations between the metrics and QoE indicators; in particular, frequent stalls are often accompanied by higher probability of early session termination. Moreover, we quantify how such correlations vary according to broadcast matches and client terminals. Some of our findings challenge intuition--e.g., we find that PC users seem more tolerant to problems than users on mobile terminals. Our results provide better understanding of user QoE and are an important step towards user QoE models in large-scale events.
机译:了解在实时互联网流期间对用户QoE进行性能下降的影响是最大化观众的关键,增加内容提供商的收入。众所周知,一些问题与低QoE有着强烈的相关性 - 例如,遇到视频摊位的用户倾向于更早地离开视频会话。然而,这主要是未知是否存在用于大规模事件的实时流动(例如,FIFA世界杯)的现场流动。这种事件特别是由于流入内容的广泛兴趣,全世界达到了令人印象深刻的高观众。我们研究大规模事件的现场流动期间的性能下降是否以及在多大程度上会影响用户的QoE。我们在2014年FIFA足球世界杯期间利用从南美的主要内容提供商收集的独特数据集。我们首先从日志中提取性能指标:流比特率,比特率交换机,播放摊位和播放启动延迟。然后,我们将这些性能度量与会话持续时间相关联,我们用作QoE指示符。我们确认指标和QoE指标之间的强烈相关性;特别是,频繁的摊位通常伴随着早期会话终止的更高概率。此外,我们量化了这种相关性如何根据广播匹配和客户终端而变化。我们的一些研究结果挑战了直觉 - 例如,我们发现PC用户似乎比移动终端上的用户更容易宽容。我们的结果提供了更好地了解用户QoE,对大规模事件中的用户QoE模型是一个重要的一步。

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