Let me Decrypt your Beauty: Real-time Prediction of Video Resolution and Bitrate for Encrypted Video Streaming

Sarah Wassermann (1) , Michael Seufert (2) , Pedro Casas (1) , Li Gang (3) , Kuang Li (3)

1 AIT - Austrian Institute of Technology [Vienna]
2 JMU - Julius-Maximilians-Universität Würzburg
3 Huawei Technologies [Shenzhen]

The dynamic adaptation of the video quality induced by HTTP Adaptive Streaming (HAS) technology introduces new Quality of Experience (QoE) metrics beyond re-buffering. In this work we address the problem of real-time QoE monitoring of HAS, focusing on the continuous prediction of video resolution and average video bitrate, for the particular case of YouTube. Through empirical evaluations over a large video dataset, we demonstrate that it is possible to accurately predict the specific video resolution, as well as the average video bitrate, both in real time, and using a time granularity as small as one new prediction every second, not achieved by other proposals in the literature.

Domaines: Apprentissage [cs.LG]; Informatique mobile; Multimédia [cs.MM]; Réseaux et télécommunications [cs.NI]

Sarah Wassermann, Michael Seufert, Pedro Casas, Li Gang, Kuang Li. Let me Decrypt your Beauty: Real-time Prediction of Video Resolution and Bitrate for Encrypted Video Streaming. Demonstrations of the Network Traffic Measurement and Analysis Conference (TMA), Jun 2019, Paris, France. ⟨hal-02134851⟩