DASH sub-representation with temporal QoE driven layering
For realizing finer granularity rate adaptation with tradeoff between visual quality and network bandwidth, DASH provides sub-representation structure that can further refine QoE options with multiple temporal quality operating point at a fixed spatial quality, i.e., PSNR. In order to facilitate streaming QoE driven decisions with sub-representations, in this paper, we shall develop a new structure for constructing DASH sub-representations. Besides, we utilize the fame significance metric to characterize the temporal quality and introduce a gradient driven temporal quality layer packing algorithm based on the frames loss induced distortion, which can be used as one theoretical foundation of constructing DASH sub-representations for matching the available throughput with the improved spatio-temporal distortion tradeoff.