Intelligent and autonomous troubleshooting is a crucial enabler for the current 5G and future 6G networks. In this work, we develop a flexible architecture for detecting anomalies in adaptive video streaming comprising three main components: i) A pattern recognizer that learns a typical pattern for video quality from the client-side application traces of a specific reference video, ii) A predictor for mapping Radio Frequency (RF) performance indicators collected on the network-side using user-based traces to a video quality measure, iii) An anomaly detector for comparing the predicted video quality pattern with the typical pattern to identify anomalies. We use real network traces (i.e., on-device measurements) collected in different geographical locations and at various times of day to train our machine learning models. We perform extensive numerical analysis to demonstrate key parameters impacting correct video quality prediction and anomaly detection. In particular, we have shown that the video playback time is the most crucial parameter determining the video quality since buffering continues during the playback and resulting in better video quality further into the playback. However, we also reveal that RF performance indicators characterizing the quality of the cellular connectivity are required to correctly predict QoE in anomalous cases. Then, we have exhibited that the mean maximum F1-score of our method is 77%, verifying the efficacy of our models. Our architecture is flexible and autonomous, so one can apply it to -- and operate with -- other user applications as long as the relevant user-based traces are available.

中文翻译：

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虚拟路测：预测自适应视频流中的 QoE 的案例

智能和自主故障排除是当前 5G 和未来 6G 网络的关键推动因素。在这项工作中，我们开发了一种灵活的架构，用于检测自适应视频流中的异常，包括三个主要组件：i) 模式识别器，从特定参考视频的客户端应用程序跟踪中学习典型的视频质量模式，ii)用于将使用基于用户的跟踪在网络侧收集的射频 (RF) 性能指标映射到视频质量度量的预测器，iii) 用于将预测的视频质量模式与典型模式进行比较以识别异常的异常检测器。我们使用在不同地理位置和一天中的不同时间收集的真实网络跟踪（即设备上的测量）来训练我们的机器学习模型。我们进行了广泛的数值分析，以展示影响正确视频质量预测和异常检测的关键参数。特别是，我们已经证明视频播放时间是决定视频质量的最关键参数，因为在播放过程中缓冲会持续进行，并在播放过程中产生更好的视频质量。然而，我们还揭示了在异常情况下正确预测 QoE 需要表征蜂窝连接质量的 RF 性能指标。然后，我们展示了我们方法的平均最大 F1 分数为 77%，验证了我们模型的有效性。我们的架构是灵活和自主的，因此只要相关的基于用户的跟踪可用，就可以将其应用于其他用户应用程序并与之一起操作。