As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for RE al-time QU ality of experience metric detection for <underline> E </underline>ncrypted T raffic— Requet —which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.

中文翻译：

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请求

由于视频流量主导互联网，运营商检测视频体验质量 (QoE) 以确保对视频流量的充分支持非常重要。随着端到端加密的广泛部署，传统的基于深度数据包检测的流量监控方法正在变得无效。这对网络运营商监控用户 QoE 并改善他们的体验提出了挑战。为了解决这个问题，我们开发并提出了一个系统回覆时刻曲<underline> 的经验度量检测乙</下划线>加密吨raffic—请求——适合网络中间盒部署。Requet 使用我们开发的检测算法从加密流量的 IP 标头中识别视频和音频块。从块统计中提取的特征用作机器学习算法的输入，以预测 QoE 指标，特别是缓冲区警告（低缓冲区、高缓冲区）、视频状态（缓冲区增加、缓冲区衰减、稳定、停止）和视频分辨率。我们收集了一个大型 YouTube 数据集，其中包含通过各种 WiFi 和 LTE 网络条件交付的各种视频资产，以评估性能。我们将 Requet 与基于先前工作的基线系统进行比较，并表明 Requet 在预测缓冲区低警告、视频状态和视频分辨率的准确度上分别优于基线系统 1.12 倍、1.53 倍和 3.14 倍。