Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones

Sarah Wassermann (1) , Nikolas Wehner (2) , Pedro Casas (2)

1 MIMOVE - Middleware on the Move
2 AIT - Austrian Institute of Technology [Vienna]

Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and user-behavior-related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.

Mots clés: Machine Learning; Smartphone Measurements; QoE

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

Sarah Wassermann, Nikolas Wehner, Pedro Casas. Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones. Workshop on AI in Networks (WAIN) 2018, Dec 2018, Toulouse, France. ⟨hal-01898083⟩