Multiple Objective Optimization for Video Streaming

In this thesis, we propose Multiple Objective Optimization (MOO) frameworks for efficient video streaming. Firstly, we introduce pre-roll delay-distortion optimization (DDO) for uninterrupted content-adaptive video streaming over low capacity, constant bitrate (CBR) channels using MOO. Content analysis is used to divide the input video into shots with assigned relevance levels. The video is adaptively encoded and streamed aiming minimum pre-roll delay and distortion with the optimal spatial and temporal resolutions and quantization parameters for each shot. With buffer and distortion constraints, the bitrate of unimportant shots is reduced to achieve an acceptable quality in important shots. Secondly, we introduce a cross-layer optimized video rate adaptation and scheduling scheme to achieve maximum “application layer” Quality-of-Service (QoS maximum video throughput (video seconds per transmission slot), and QoS fairness for wireless video streaming. Using the MOO framework, these objectives are jointly optimized such that the user with i) the least remaining playback time, ii) highest available video throughput and iii) maximum video quality is served. Finally, we propose an adaptive framework for compression and streaming of stereo video using the existing network infrastructure. We employ content-adaptive stereo video coding (CA-SC), where additional compression is achieved by spatial and/or temporal downsampling depending on the content. An end-to-end streaming system where the end-users can view the video in mono or stereo mode depending on their display capabilities is implemented and MOO formulations are proposed. The improvements achieved are demonstrated with experimental results.