Video QoE Metrics from Encrypted Traffic: Application-agnostic Methodology

• URL: http://arxiv.org/abs/2504.14720v1
• Date: Sun, 20 Apr 2025 19:18:13 GMT
• Title: Video QoE Metrics from Encrypted Traffic: Application-agnostic Methodology
• Authors: Tamir Berger, Jonathan Sterenson, Raz Birman, Ofer Hadar,
• Abstract summary: We propose an application-agnostic approach for objective QoE estimation from encrypted traffic.<n>We obtained key video QoE metrics, enabling broad applicability to various proprietary IMVCAs and VCAs.<n>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.
• Score: 2.7123995549185325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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