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


Chao Chen, Lark Kwon Choi, Gustavo de Veciana, Constantine Caramanis, Robert W. Heath Jr., and Alan C. Bovik


Proc. of ICASSP, pp. 3602-3606, May. 26-31, 2013.


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. Therefor, 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.