Forecasting YouTube QoE over SATCOM

Matthieu Petrou (1) , David Pradas (1) , Mickaël Royer (2) , Emmanuel Lochin (2)

1 Viveris Technologies
2 ENAC - Ecole Nationale de l'Aviation Civile

We investigate the feasibility of using machine learning methods for predicting the Quality of Experience (QoE) of end users in the context of video streaming over satellite networks. To achieve this, we analyzed QoE and traffic data from 2,400 YouTube video sessions over emulated geosynchronous (GSO) satellite links. The objective is to determine whether existing learning methods, originally developed for wired or mobile networks, can be adapted to accurately predict key QoE factors over SATCOM. We particularly investigate a specific existing framework, which achieves outstanding performance in predicting resolution and initial delay. However, we point out some discrepancies in their hypothesis, leading to optimistic forecasting results. We then refine their methodology to ensure a complete independence between training and test datasets, leading to a fairer QoE video streaming forecast over satellite networks.

Mots clés: SATCOM; QoE; QoS; network monitoring; HTTP adaptive video streaming; machine learning; deep learning

Domaines: Réseaux et télécommunications [cs.NI]

Matthieu Petrou, David Pradas, Mickaël Royer, Emmanuel Lochin. Forecasting YouTube QoE over SATCOM. The 97th Vehicular Technology Conference (VTC 2023 Spring), IEEE, Jun 2023, Florence, Italy. ⟨hal-04083292⟩