Video akıtımı için çok hedef işlevli eniyileme

In this thesis, we propose Multiple Objective Optimization (MOO) frameworks for efficient videostreaming.Firstly, we introduce pre-roll delay-distortion optimization (DDO) for uninterrupted content-adaptive video streaming over low capacity, constant bitrate (CBR) channels using MOO. Contentanalysis is used to divide the input video into shots with assigned relevance levels. The video isadaptively encoded and streamed aiming minimum pre-roll delay and distortion with the optimalspatial and temporal resolutions and quantization parameters for each shot. With buffer and distortionconstraints, the bitrate of unimportant shots is reduced to achieve an acceptable quality in importantshots.Secondly, we introduce a cross-layer optimized video rate adaptation and scheduling schemeto achieve maximum ``application layer" Quality-of-Service (QoS), maximum video throughput (videoseconds per transmission slot), and QoS fairness for wireless video streaming. Using the MOOframework, these objectives are jointly optimized such that the user with i) the least remainingplayback 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 videousing 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 thecontent. An end-to-end streaming system where the end-users can view the video in mono or stereomode depending on their display capabilities is implemented and MOO formulations are proposed.The improvements achieved are demonstrated with experimental results.