|Institution:||University of Texas – Austin|
|Department:||Electrical and Computer Engineering|
|Keywords:||Video quality assessment; Quality of experience; Video streaming; Scalable video; Wireless communication|
|Full text PDF:||http://hdl.handle.net/2152/23203|
Video traffic is growing rapidly in wireless networks. Different from ordinary data traffic, video streams have higher data rates and tighter delay constraints. The ever-varying throughput of wireless links, however, cannot support continuous video playback if the video data rate is kept at a high level. To this end, adaptive video transmission techniques are employed to reduce the risk of playback interruptions by dynamically matching the video data rate to the varying channel throughput. In this dissertation, I develop new models to capture viewers' quality of experience (QoE) and design adaptive transmission algorithms to optimize the QoE. The contributions of this dissertation are threefold. First, I develop a new model for the viewers' QoE in rate-switching systems in which the video source rate is adapted every several seconds. The model is developed to predict an important aspect of QoE, the time-varying subjective quality (TVSQ), i.e., the up-to-the-moment subjective quality of a video as it is played. I first build a video database of rate-switching videos and measure TVSQs via a subjective study. Then, I parameterize and validate the TVSQ model using the measured TVSQs. Finally, based on the TVSQ model, I design an adaptive rate-switching algorithm that optimizes the time-averaged TVSQs of wireless video users. Second, I propose an adaptive video transmission algorithm to optimize the Overall Quality (OQ) of rate-switching videos, i.e., the viewers' judgement on the quality of the whole video. Through the subjective study, I find that the OQ is strongly correlated with the empirical cumulative distribution function (eCDF) of the video quality perceived by viewers. Based on this observation, I develop an adaptive video transmission algorithm that maximizes the number of video users who satisfy given constraints on the eCDF of perceived video qualities. Third, I propose an adaptive transmission algorithm for scalable videos. Different from the rate-switching systems, scalable videos support rate adaptation for each video frame. The proposed adaptive transmission algorithm maximizes the time-averaged video quality while maintaining continuous video playback. When the channel throughput is high, the algorithm increases the video data rate to improve video quality. Otherwise, the algorithm decreases the video data rate to buffer more videos and to reduce the risk of playback interruption. Simulation results show that the performance of the proposed algorithm is close to a performance upper bound.