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

Abstract : 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.
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Submitted on : Thursday, October 18, 2018 - 2:59:20 AM
Last modification on : Wednesday, June 8, 2022 - 12:50:04 PM
Long-term archiving on: : Saturday, January 19, 2019 - 12:21:13 PM


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  • HAL Id : hal-01898083, version 1



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⟩



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