你所看到的就是你得到的:测量ABR视频流QoE通过设备屏幕记录

Shichang Xu, E. Petajan, S. Sen, Z. Morley Mao
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引用次数: 3

摘要

分析蜂窝网络上自适应比特率(ABR)流传输的QoE对于包括内容提供商和移动网络提供商在内的许多实体至关重要。然而,现有的方法大多依赖于网络流量分析。除了潜在的准确性问题外,端到端网络流量加密的日益普及也给它们带来了挑战。在本文中,我们探索了一种非常不同的QoE测量方法——利用商用设备上广泛可用的屏幕记录功能来记录显示在移动设备屏幕上的视频,并分析录制的视频来测量交付的QoE。我们设计了一个新的系统VideoEye来进行这种基于屏幕记录的QoE分析。我们确定了所涉及的各种技术挑战,包括由屏幕记录过程引入的失真,这可能使这种分析变得困难。我们开发的技术,以准确地测量视频QoE从屏幕记录,即使在记录失真的存在。我们的评估表明,VideoEye准确地检测到重要的QoE指标,包括在不同时间点播放的曲目和失速统计。检测到的失速持续时间的最大误差为0.5秒。检测显示航迹的准确率大于97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What you see is what you get: measure ABR video streaming QoE via on-device screen recording
Analyzing delivered QoE for Adaptive Bitrate (ABR) streaming over cellular networks is critical for a host of entities including content providers and mobile network providers. However, existing approaches mostly rely on network traffic analysis. In addition to potential accuracy issues, they are challenged by the increasing use of end-to-end network traffic encryption. In this paper, we explore a very different approach to QoE measurement --- utilizing the screen recording capability widely available on commodity devices to record the video displayed on the mobile device screen, and analyzing the recorded video to measure the delivered QoE. We design a novel system VideoEye to conduct such screen-recording-based QoE analysis. We identify the various technical challenges involved, including distortions introduced by the screen recording process that can make such analysis difficult. We develop techniques to accurately measure video QoE from the screen recordings even in the presence of recording distortions. Our evaluations demonstrate that VideoEye accurately detects important QoE indicators including the track played at different points in time, and stall statistics. The maximal error in detected stall duration is 0.5 s. The accuracy of detecting the displayed tracks is higher than 97%.
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