Rate-distortion optimized video streaming using conditional packet delay distributions

摘要

We introduce a formulation for the rate-distortion optimized transmission scheduling of packetized video that incorporates a new, more accurate model for Internet packet delay and loss. The model assumes that delays at successive transmission times form a 1st order Markov random process. The model uses feedback to learn about delays in the recent past, and finds delay distributions for later transmissions by chaining together approximated conditional delay distributions. In prior work, this model has proved to be more accurate than the iid /spl Gamma/ delay model commonly used for rate-distortion optimized streaming. In this work, we show how to use the new delay and loss model to perform rate-distortion optimized packet transmission scheduling. Our results show a more than 1 dB gain in scheduling performance for our new delay model over the iid /spl Gamma/ model, for streaming simulations conducted with measured Internet delay and loss traces.