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Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation

机译：基于机器学习的KPI视频流估计视频流流量监测

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摘要

Quality of Experience (QoE) monitoring of video streaming traffic is a crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.
机译：视频流媒体流量的经验质量（QoE）监测是服务提供商的一个重要任务。如今，由于端到端加密的使用增加，这是挑战。为了克服这个问题，在近年来QoE监测的机器学习（ML）方法在近年来越来越受欢迎。这项工作提出了一种框架，该框架包括一种机器学习管道，该管道可以用于检测Qoe QoE相关事件，例如缓冲事件和视频分辨率的改变，以实时为正在进行的YouTube视频流节点。为此目的，使用从Android设备收集的YouTube流流量训练了ML模型。稍后，培训的ML模型部署在框架的管道中以进行在线预测。 ML模型使用从网络层观察的统计流量信息来学习和预测视频QoE相关事件。预测视频事件，它达到了88％的总体测试准确性。虽然我们的工作尚未在早期阶段，但ML模型在线检测和预测视频事件的应用产生了非常有前景的结果。

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