This research investigates scalable video communications and its applications to video streaming, conferencing and 3DTV. Scalable video coding (SVC) is a layer-based encoding scheme that provides spatial, temporal and quality scalability. Heterogeneity of the Internet and clients' operating environment necessitate the adaptation of media content to ensure a satisfactory multimedia experience. SVC's layer structure allows the extraction of partial bitstreams at reduced spatial, quality and temporal resolutions that adjust the media bitrate at a fine granularity to changes in network state. The main focus of this research work is in developing such extraction algorithms in the context of SVC. Based on a combination of metadata computations and prediction mechanisms, these algorithms evaluate the quality contribution of each layer in the SVC bitstream and make extraction decisions that are aimed at maximizing video quality while operating within the available bandwidth resources. These techniques are applied in two-way interaction and one-way streaming of 2D and 3D content. Depending on the delay tolerance of these applications, rate-distortion optimized extraction algorithms are proposed. For conferencing applications, the extraction decisions are made over single frames and frame pairs due to tight end-to-end delay constraints. The proposed extraction algorithms for 3D content streaming maximize the overall perceived 3D quality based on human stereoscopic perception. When compared to current extraction methods, the new algorithms offer better video quality at a given bitrate while performing lesser number of metadata computations in the post-encoding phase. The solutions proposed for each application achieve the recurring goal of maintaining the best possible level of end-user quality of multimedia experience in spite of network impairments.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/42732 |
| Date | 19 August 2011 |
| Creators | Palaniappan, Ramanathan |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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