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Characterizing QoE in Large-Scale Live Streaming

机译:大规模直播中的QoE表征

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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:了解实时Internet流传输期间性能下降对用户的QoE的影响是最大化受众和增加内容提供商收入的关键。众所周知,有些问题与较低的QoE密切相关-例如,遇到视频停顿的用户倾向于更早地离开视频会话。但是,对于大型活动的现场直播(例如FIFA世界杯),这种观察是否成立尚无定论。由于对流媒体内容的广泛兴趣,这种事件尤其突出,吸引了全世界令人印象深刻的高受众。我们研究了大规模事件的实时流传输期间性能下降是否以及在多大程度上影响了用户的QoE。在2014年FIFA足球世界杯期间,我们利用从南美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,比特率切换,播放停顿和播放启动延迟。然后,我们将这些性能指标与会话持续时间相关联,我们将其用作QoE指标。我们确认了指标与QoE指标之间的强相关性;特别是频繁的停顿通常伴随着提前终止会话的可能性更高。而且,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。我们的一些发现挑战了直觉-例如,我们发现PC用户似乎比移动终端上的用户更能容忍问题。我们的结果提供了对用户QoE的更好理解,并且是在大规模事件中朝用户QoE模型迈出的重要一步。

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