Objective quality definition of scalable video coding and its application for optimal streaming of FGS-coded videos

With the proliferation of video contents widely distributed over the Internet and the progress of video coding (e.g., H.264/AVC) and transmission technologies, more challenges need to overcome in order to meet the requirements of all users with diverse terminals. Video streaming over IP and wireless becomes a popular issue since the new century.However, there is little work concerning the quantitative analysis on the objective quality of streaming videos. Thus a strict definition of the objective quality and quality variation of scalable video coding (SVC) is required, in order to efficiently transmit video contents over Internet and wireless and reach an attainable subjective quality perception for end-users. Since FGS (fine granularity scalability) video is coded in bit planes, its enhancing layer can be truncated arbitrarily, as a case study of scalable video coding, an objective quality definition for FGS-coded video is introduced in this paper, based on MSE (mean square error) and PSNR (peak signal-to-noise ratio). This definition can also be generalized to any layered scalable coding videos, such as the traditional layered videos in BL + ELs (base layer + enhancing layers) formats or H.264/AVC in BL + CGS (coarse granularity scalability) + FGS structures, and it can be applied to design optimal algorithms for video streaming. Furthermore, It can also be taken as a measure to assess the subjective quality of streaming videos, by incorporating user preferences and terminal capacities.According to this definition, a quality optimal problem of scene in video segments is formulated and solved using the state transfer graph and dynamic programming. The optimal transmission policy is also obtained and compared with a real-time transmission algorithm. Different aggregation levels (segmentation granularity) of video segments for optimal transmission are also examined by experimental data. Simulation results validate our observations.