An adaptive-predictive architecture for video streaming servers

Providing perceptually good quality video streaming over today's Internet is a complex task, as the available bandwidth and the encoded video rate can exhibit significant variability at different time scales. This research investigates the ways that such mismatches can be addressed, suggesting that this can be done by minimizing quality variability and increasing the overall video quality rendered to end-systems. Towards this end, we formulate a combination of innovative congestion control-aware mechanisms and filtering techniques. Our proposal involves extracting explicit information from the network, as well as providing the video source with consistent and stable information. We investigate this proposal throughout the following pages and reveal its efficiency by simulating representative scenarios.

► Available bandwidth and video encoding rate exhibit high variability at different time scales. ► We design a solution for scalable video streaming over explicit feedback networks. ► Streaming server adapts to network conditions in a fine-grain manner. ► It combines congestion control mechanisms with filtering and prediction techniques. ► With our approach, the mean video quality has less statistically significant variability.