Online buffer fullness estimation aided adaptive media playout for video streaming

Adaptive media playout (AMP) control is proposed in order to compensate for the bit-rate fluctuation of networks, which may result in playout interruptions in video streaming application. Most AMP algorithms found in the literature trigger playout-rate adjustments based on the buffer fullness or its variation. However, the challenge of the threshold based methods is to select the appropriate threshold for triggering a playout-rate adjustment owing to the unknown fluctuation of the channel quality and the video bitrate. We conceive an adaptive media playout regime based on underflow probability estimation, which requires no significant statistical knowledge of the previous tele-traffic load. To achieve this, we present an underflow probability estimation model based on large deviation theory relying on the buffer fullness and on its variation. We will then directly use the underflow probability to trigger the actions of playout control, instead of using indirect methods based on a buffer fullness threshold or buffer fullness variation threshold. Experiments based on MPEG-4 Variable Bit-Rate encoded video and VBR channels associated with Adaptive Modulation and Coding are conducted in order to investigate the achievable performance of the proposed algorithm. Our simulation results demonstrate an improved performance in comparison to other recent AMP algorithms.