On user-centric analysis and prediction of QoE for video streaming using empirical measurements

Abstract

Assessing the impact of different network conditions on user experience is important for improving the telecommunication services. We have developed a modular framework that includes monitoring and data collection tools and algorithms for user-centric analysis and prediction of the QoE in video streaming. The MLQoE employs several machine learning (ML) algorithms and tunes their hyper-parameters. It dynamically selects the ML algorithm that exhibits the best performance and its parameters automatically based on the input (e.g., network and systems metrics). We applied the MLQoE for predicting the QoE of the video streaming service in the context of two field studies, one performed in the production environment of a large telecom operator and the other at our Institute. The analysis indicated the parameters with the dominant impact on the perceived QoE and revealed that the QoE vary across users. This motivates the use of customized adaptation mechanisms in video streaming under network performance degradation. The MLQoE results in fairly accurate predictions e.g., a median error in predicting the QoE of 0.0991 and 0.5517 in the first (second) field study, respectively, on the MOS scale.