A novel parallel encoding framework for scalable video coding

Kai Yao, Jun Sun, Jiaying Liu, Zongming Guo, Longshe Huo

Abstract: In this paper, we first propose a new parallel video coding framework, considering three important factors: parallel strategy, computational complexity and task scheduling. Then combining the characteristics of scalable video coding (SVC), a novel parallel encoding structure for temporal and quality scalabilities is introduced to obtain a high speedup of parallel SVC. Since the data dependencies in SVC are complex and time variant, the scheduling of parallel SVC is extremely difficult. In order to find the optimal scheduling solution, directed acyclic graph (DAG) is exploited to model the dependencies of encoding tasks, and the complexities of the encoding tasks which are accurately estimated by the Kalman filter to weight the scheduling tasks. Finally, two heuristic scheduling algorithms are also proposed to achieve a high encoding speed of parallel SVC. Experimental results show that the speedup of our parallel method was higher (about 60%) than previous work. Using the proposed method, high definition (HD) SVC videos can be encoded in real time.