MIMIC: Using passive network measurements to estimate HTTP-based adaptive video QoE metrics
MIMIC: Using passive network measurements to estimate HTTP-based adaptive video QoE metrics
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DOI：
10.23919/tma.2017.8002920
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发表时间：
2017-06
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影响因子：
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通讯作者：
Tarun Mangla;Emir Halepovic;M. Ammar;E. Zegura
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作者：
Tarun Mangla;Emir Halepovic;M. Ammar;E. Zegura
基于HTTP的自适应流（HAS）在蜂窝网络中已经看到了主要的增长。作为关键应用和网络需求驱动因素，视频流的用户感知体验质量（QoE）有助于整体用户满意度。因此，蜂窝网络运营商了解视频流的QoE变得至关重要。它可以帮助进行长期网络规划和配置以及QoE感知流量管理。然而，跟踪QoE具有挑战性，因为网络运营商无法直接访问视频流应用程序、用户设备或服务器。在本文中，我们提供了一种方法，使用被动网络测量未加密的HAS视频流估计三个关键的视频QoE指标-平均比特率，重新缓冲率和比特率开关。我们的方法依赖于HAS的语义来模拟客户端上的视频会话。我们首先在实验室中通过受控实验开发和验证我们的方法。然后，我们使用来自主要蜂窝运营商的网络数据和来自大型视频服务的地面真实QoE指标对我们的方法进行大规模验证。对于70%-90%的视频会话，我们准确地预测平均比特率的值在10%的相对误差内，对于65-90%的会话，重新缓冲率在1个百分点内。我们进一步量化了由于视频块替换引起的网络开销，并观察到大量会话具有20%或更多的高开销。最后，我们强调了在大规模监控系统中视频QoE指标估计的几个挑战。
HTTP-based Adaptive Streaming (HAS) has seen a major growth in the cellular networks. As a key application and network demand driver, user-perceived Quality of Experience (QoE) of video streaming contributes to the overall user satisfaction. Therefore, it becomes critical for the cellular network operators to understand the QoE of video streams. It can help with long-term network planning and provisioning and QoE-aware traffic management. However, tracking QoE is challenging as network operators do not have direct access to the video streaming apps, user devices or servers. In this paper, we provide a methodology that uses passive network measurements of unencrypted HAS video streams to estimate three key video QoE metrics — average bitrate, re-buffering ratio and bitrate switches. Our approach relies on the semantics of HAS to model a video session on the client. We first develop and validate our methodology through controlled experiments in the lab. Then, we conduct a large-scale validation of our approach using network data from a major cellular operator and ground truth QoE metrics from a large video service. We accurately predict the value of average bitrate within a relative error of 10% for 70%–90% of video sessions and re-buffering ratio within 1 percentage point for 65–90% of sessions. We further quantify the network overhead due to video chunk replacement and observe that a significant number of sessions have a high overhead of 20% or more. Finally, we highlight several challenges with video QoE metrics estimation in a large-scale monitoring system.