Adaptive parallel video-coding algorithm

Abstract

Parallel encoding of video inevitably frame rate gives varying rate performance due to dynamically changing video content and motion field since the encoding process of each macro-block, especially motion estimation, is data dependent. A multiprocessor schedule optimized for a particular frame with certain macro-block encoding time may not be optimized towards another frame with different encoding time, which causes performance degradation to the parallelization. To tackle this problem, we propose a method based on a batch of near-optimal schedules generated at compile-time and a run-time mechanism to select the schedule giving the shortest predicted critical path length. This method has the advantage of being near-optimal using compile-time schedules while involving only run-time selection rather than re-scheduling. Implementation on the IBM SP2 multiprocessor system using 24 processors gives an average speedup of about 13.5 (frame rate of 38.5 frames per second) for a CIF sequence consisting of segments of 6 different scenes. This is equivalent to an average improvement of about 16.9% over the single schedule scheme with schedule adapted to each of the scenes. Using an open test sequence consisting of 8 video segments, the average improvement achieved is 13.2%, i.e. an average speedup of 13.3 (35.6 frames per second).