Quality of experience (QoE) that serves as a direct evaluation of viewing experience from the end users is of vital importance for network optimization, and should be constantly monitored. Unlike existing video-on-demand streaming services, real-time interactivity is critical to the mobile live broadcasting experience for both broadcasters and their audiences. While existing QoE metrics that are validated on limited video contents and synthetic stall patterns have shown effectiveness in their trained QoE benchmarks, a common caveat is that they often encounter challenges in practical live broadcasting scenarios, where one needs to accurately understand the activity in the video with fluctuating QoE and figure out what is going to happen to support the real-time feedback to the broadcaster. In this paper, we propose a temporal relational reasoning guided QoE evaluation approach for mobile live video broadcasting, namely TRR-QoE , which explicitly attends to the temporal relationships between consecutive frames to achieve a more comprehensive understanding of the distortion-aware variation. In our design, video frames are first processed by deep neural network (DNN) to extract quality-indicative features. Afterwards, besides explicitly integrating features of individual frames to account for the spatial distortion information, multi-scale temporal relational information corresponding to diverse temporal resolutions are made full use of to capture temporal-distortion-aware variation. As a result, the overall QoE prediction could be derived by combining both aspects. The results of experiments conducted on a number of benchmark databases demonstrate the superiority of TRR-QoE over the representative state-of-the-art metrics.

中文翻译：

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时间现场指导的移动实时视频广播的QoE评估

体验质量（QoE）可直接评估最终用户的观看体验，对于网络优化至关重要，因此应不断对其进行监控。与现有的视频点播流服务不同，实时交互性对于广播公司及其观众的移动实时广播体验至关重要。尽管在有限的视频内容和合成停顿模式上经过验证的现有QoE指标在其训练有素的QoE基准中已显示出有效性，但一个常见的警告是，它们在实际的直播环境中经常遇到挑战，在这种情况下，人们需要准确地了解视频中的活动QoE的波动，并弄清楚将发生什么情况以支持向广播公司的实时反馈。在本文中，TRR质量 ，它明确关注连续帧之间的时间关系，以实现对失真感知变化的更全面的理解。在我们的设计中，视频帧首先由深度神经网络（DNN）处理，以提取质量指示特征。然后，除了明确地整合各个帧的特征以解决空间失真信息之外，还充分利用了与各种时间分辨率相对应的多尺度时间关系信息来捕获感知时间失真的变化。结果，可以通过组合两个方面来得出总体QoE预测。在多个基准数据库上进行的实验结果证明了该方法的优越性。TRR质量 具有代表性的最新指标。