Modeling the Time-varying Subjective Quality of HTTP Video Streams with Rate Adaptations
This work addresses the need for accurate Quality of Experience (QoE) prediction in HTTP video streaming to improve efficiency, but it is incremental as it applies an existing model type to a specific domain.
The paper tackles the problem of predicting the time-varying subjective quality (TVSQ) of rate-adaptive HTTP video streams, which is difficult due to hysteresis effects and nonlinearities in human responses, and presents a Hammerstein-Wiener model that reliably predicts TVSQ with a simple structure suitable for online use.
Newly developed HTTP-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' Quality of Experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer-duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for on-line TVSQ prediction in HTTP based streaming.