Rate allocation for prediction drift reduction in video streaming

In SNR-scalable motion compensated video coders, prediction drift is introduced when decoding below the rate at which the encoder loop operates. Prediction drift reduces the coding efficiency and changes the quality of the video over time which is very annoying visually. In this paper, we propose a rate allocation algorithm to reduce these quality variations. By modeling both the video signal and the coder, we analyze the efficiency of the coder and find the rate allocation which creates frames which have the same distortion when decoded. We have compared numerical simulations of our algorithm with experimental results from real video encodings. The results show that by using a proper rate allocation algorithm quality fluctuations can be reduced.