Estimating QoE from Encrypted Video Conferencing Traffic

Michael Sidorov ¹, Raz Birman ¹, Ofer Hadar ¹

⁰School of Electrical and Computer Engineering, Ben Gurion University of the Negev, Be'er Sheba 8410501, Israel.

Sensors (basel, Switzerland) +

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February 26, 2025

View abstract on PubMed

Summary

This study predicts video conferencing Quality of Experience (QoE) using encrypted traffic. Machine learning models accurately estimate key indicators like frames per second (FPS) and resolution (R), improving internet security analysis.

Area Of Science

Computer Science
Network Engineering
Information Security

Background

Internet security relies on traffic encryption, which hinders analysis of applications like video delivery optimization and Quality of Experience (QoE) estimation.
Existing QoE prediction models primarily focus on clear-text traffic and often provide low-resolution, categorical predictions, leaving the video conferencing (VC) domain underexplored.

Purpose Of The Study

To address the challenges in QoE assessment for encrypted traffic in video conferencing (VC) applications.
To develop precise, continuous QoE predictions for VC, moving beyond broad categories.
To analyze encrypted traffic data for improved QoE estimation.

Main Methods

Analysis of a large dataset comprising Zoom sessions.
Training and evaluation of five classical machine learning (ML) models.
Development and training of two custom deep neural networks (DNNs).
Prediction of three key QoE indicators: frames per second (FPS), resolution (R), and Naturalness Image Quality Evaluator (NIQE).
Utilizing a 10-fold cross-validation technique for robust model assessment.

Main Results

Achieved mean error rates of 8.27% for FPS prediction.
Achieved mean error rates of 7.56% for resolution (R) prediction.
Achieved mean error rates of 2.08% for Naturalness Image Quality Evaluator (NIQE) prediction.

Conclusions

The developed models demonstrate significant advancements in QoE assessment for encrypted traffic within VC applications.
Precise, continuous QoE predictions are achievable even with encrypted video conferencing data.
This research provides a foundation for enhanced network management and user experience in video conferencing services.

Keywords:

deep learning encrypted traffic machine learning quality of experience video conferencing