engleski

Towards a Framework for Classifying YouTube QoE Based on Monitoring of Encrypted Traffic

With the move to traffic encryption adopted by many Over The Top (OTT) providers of video distribution services, Internet Service Providers (ISPs) are now facing the challenges of monitoring application performance and potential end user perceived service quality degradations. With lack of direct feedback from OTT providers, ISPs generally rely on passive traffic monitoring solutions deployed within their network for the purposes of monitoring OTT service performance. In this paper we describe our ongoing research efforts aimed at investigating solutions for estimating end user QoE when watching YouTube videos, based solely on the analysis of encrypted traffic in mobile and WiFi networks. We shortly describe our developed YouQ system which enables the monitoring of both application-layer KPIs and encrypted network traffic for the purpose of developing ML-based QoE classification models. We discuss ongoing and future work in the direction of developing a more general framework for the estimation of video streaming QoE based on further enhancements of the YouQ system. The framework aims to support the collection of data across different end user device platforms and access networks, and the analysis of both TCP and QUIC traffic.

Quality of Experience ; Video Streaming ; HTTP adaptive streaming ; YouTube ; network measurements ; passive monitoring ; machine learning

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