A dynamic system model of time-varying subjective quality of video streams over HTTP

Newly developed HTTP-based video streaming technology enables flexible rate-adaptation in varying channel conditions. The users' Quality of Experience (QoE) of rate-adaptive HTTP video streams, however, is not well understood. Therefore, designing QoE-optimized rate-adaptive video streaming algorithms remains a challenging task. An important aspect of understanding and modeling QoE is to be able to predict the up-to-the-moment subjective quality of video as it is played. We propose a dynamic system model to predict the time-varying subjective quality (TVSQ) of rate-adaptive videos that is transported over HTTP. For this purpose, we built a video database and measured TVSQ via a subjective study. A dynamic system model is developed using the database and the measured human data. We show that the proposed model can effectively predict the TVSQ of rate-adaptive videos in an online manner, which is necessary to be able to conduct QoE-optimized online rate-adaptation for HTTP-based video streaming.