无需解密即可从网络流量中估算视频体验质量，适配各类应用。
Video QoE Metrics from Encrypted Traffic: Application-agnostic Methodology
- 基于加密流量设计通用算法，不依赖具体视频应用
- 在25,680秒数据上实现85.2%的帧率预测准确率（误差±2帧）
- 适用于缺乏用户端体验数据的网络运营商，尤其适合加密通信场景
即时通讯视频应用（IMVCAs）和视频会议应用（VCAs）已成为现代通信的核心。网络条件对用户体验质量（QoE）影响显著，但运营商因流量加密无法获取终端侧的QoE指标。现有方法尝试从加密流量中估计QoE，最先进方案采用机器学习模型，但缺乏真实标签数据制约训练与验证。为此，本文提出一种应用无关的客观QoE估算方法，独立于具体视频应用，可广泛适配各类专有应用。为验证有效性，我们构建了涵盖多种网络条件的WhatsApp视频会话数据集，含25,680秒流量数据及对应QoE指标。评估显示，该方法在全数据集上表现优异：帧率预测准确率达85.2%（误差范围±2 FPS），基于PIQE的质量评分分类准确率为90.2%。
原文摘要 · Abstract (English)
Instant Messaging-Based Video Call Applications (IMVCAs) and Video Conferencing Applications (VCAs) have become integral to modern communication. Ensuring a high Quality of Experience (QoE) for users in this context is critical for network operators, as network conditions significantly impact user QoE. However, network operators lack access to end-device QoE metrics due to encrypted traffic. Existing solutions estimate QoE metrics from encrypted traffic traversing the network, with the most advanced approaches leveraging machine learning models. Subsequently, the need for ground truth QoE metrics for training and validation poses a challenge, as not all video applications provide these metrics. To address this challenge, we propose an application-agnostic approach for objective QoE estimation from encrypted traffic. Independent of the video application, we obtained key video QoE metrics, enabling broad applicability to various proprietary IMVCAs and VCAs. To validate our solution, we created a diverse dataset from WhatsApp video sessions under various network conditions, comprising 25,680 seconds of traffic data and QoE metrics. Our evaluation shows high performance across the entire dataset, with 85.2% accuracy for FPS predictions within an error margin of two FPS, and 90.2% accuracy for PIQE-based quality rating classification.
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