Scalable video transmission

Abstract

To provide enhanced multimedia services for heterogeneous networks and terminal devices, Scalable Video Coding (SVC) has been developed to embed different quality of video in a single bitstream. Similar to classical compressed video transmission, different packets of a video bitstream have different impacts on received video quality. Therefore, distortion modeling and estimation are necessary in designing a robust video transmission strategy under various network conditions. In the paper, we present the first scheme of packet loss induced distortion modeling and estimation in SVC transmission. The proposed scheme is applicable to numerous video communication and networking scenarios in which accurate distortion information can be utilized to enhance the performance of video transmission. One major challenge in scalable video distortion estimation is due to the adoption of more complicated prediction structure in SVC, which makes the tracking of error propagation much more difficult than the non-scalable encoded video. In this research, we tackle such challenge by systematically tracking the propagation of errors under various prediction trajectories. Supplemental information about the compressed video is embedded into data packets to substantially simplify the modeling and estimation. Moreover, with supplemental data of inter prediction information, distortion estimation can be processed without parsing video bitstream which results in much lower computation and memory cost. With negligible effects on the data size, experimental results show that the proposed scheme is able to track and estimate the distortion with very high accuracy. This first ever scalable video transmission distortion modeling and estimation scheme can be deployed at either gateways or receivers because of its low computation and memory cost.