MDC adaptive video streaming

Multiple Description Coding (MDC) is designed for multiple path video streaming with channel diversities. In this thesis, we investigate the performance of multi-path video streaming using the MDC technique. The MDC frame loss rate is one of the indicators of the real time video quality. A classification based framework for making mode decisions to minimize the MDC video frame transmission cost that may be defined in terms of the six parameters, number of sub-streams, number of transmission channels, GOP length, the I-frame positions, probability of network transmission states and probability of transmission changes. This thesis surveys the current status of horizontal decomposition into distributed computation, and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, and video coding. The focus of this thesis is on the video adaptive coding process to improve performance in terms of one or more of these factors. How to deliver a real-time MDC video from an end user over multi-channels is studied. The traffic is used to probe the network on determinig the network conditions and optimizing the coding algorithms appropriately. An efficient transmission statistical model Auto Regression (AR) to capture the properites of the region of interest is also introduced. Both the mode decisions and the error concealment require feedback from the network regarding the available bandwidth, loss probability, video coding methods and coding time spatial manners. The proposed algorithm works in a fully distributed environment, making it suitable for wireless ad hoc networks or other IP networks.