|M Hammad Mazhar||University of Iowa, USA|
|M Zubair Shafiq||University of Iowa, USA|
The widespread deployment of end-to-end encryp-tion protocols such as HTTPS and QUIC has reduced the visibility for operators into traffic on their networks. Network operators need the visibility to monitor and mitigate Quality of Experience (QoE) impairments in popular applications such as video streaming. To address this problem, we propose a machine learning based approach to monitor QoE metrics for encrypted video traffic. We leverage network and transport layer information as features to train machine learning classifiers for inferring video QoE metrics such as startup delay and rebuffering events. Using our proposed approach, network operators can detect and react to encrypted video QoE impairments in real-time. We evaluate our approach for YouTube adaptive video streams using HTTPS and QUIC. The experimental evaluations show that our approach achieves up to 90% classification accuracy for HTTPS and up to 85% classification accuracy for QUIC.