Forecasting YouTube QoE over SATCOM - ENAC - École nationale de l'aviation civile Accéder directement au contenu
Communication Dans Un Congrès Année :

Forecasting YouTube QoE over SATCOM

Matthieu Petrou
  • Fonction : Auteur
  • PersonId : 1128320
David Pradas
  • Fonction : Auteur
  • PersonId : 1128321
Mickaël Royer
  • Fonction : Auteur
  • PersonId : 1128322
Emmanuel Lochin


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.
Fichier principal
Vignette du fichier
Forecasting YouTube QoE over SATCOM.pdf (950.83 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04083292 , version 1 (27-04-2023)


  • HAL Id : hal-04083292 , version 1


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⟩
15 Consultations
17 Téléchargements


Gmail Facebook Twitter LinkedIn More