  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.
