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To each according to his needs: Dimensioning video buffer for specific user profiles and behavior

Today’s video streaming platforms offer videos in a variety of quality settings in order to attract as many users as possible. But even though a sufficiently dimensioned¬†network¬†can not always be provided for the best experience, users are asking for high QoE. Users consume the content of a video streaming platform in different ways, while video delivery platforms currently do not account for these scenarios and thus ensure at best mediocre QoE. In this paper, we develop a queuing model and provide a mean-value analysis to investigate the impact of user profiles on the QoE of HTTP Video Streaming for typical user scenarios.

Our results show that the user profile and particularly the scenario have to be respected when dimensioning the buffer. Further, we present recommendations on how to adapt player parameters in order to optimize the QoE for individual users profiles and viewing habits. The provided model leads to relevant insights that are required to build a system that guarantees each user the best attainable QoE.