Predicting slice loss distortion in H.264/AVC video for low complexity data prioritization

We propose a low complexity Generalized Linear Model (GLM) for prioritizing slices during real-time H.264/AVC compressed video streaming. We train the GLM over a video database to predict the Cumulative Mean Square Error (CMSE) corresponding to individual slice losses by using a combination of efficient video parameters which can be easily extracted during the encoding of a frame. We prioritize the slices generated within a Group of Pictures (GOP) based on the predicted CMSE by using a Quartile Based Prioritization (QBP) scheme. For comparison, we also perform QBP on measured CMSE values from individual pre-encoded slice losses and analyze the priority misclassifications of the slices. We validate our model by applying Unequal Error Protection (UEP) using RCPC codes to the different prioritized bitstreams and evaluating their performance over noisy channels. Simulation results show that predicted CMSE schemes achieve PSNR performance close to that of the measured CMSE schemes for different slice sizes, video bitrates and over different channel SNRs.