Parallelizing video transcoding using Map-Reduce-based cloud computing

Due to the complexity of video coding, fast transcoding is still a challenge. Various parallel coding methods have been proposed. In this paper, we present a parallel transcoding system over Map/Reduce cloud computing architecture. Input video sequences are divided into segments, and mapped to multiple computers. The sub-tasks are launched in parallel with processing results concatenated to the final output sequences. For heterogeneous clips, computing capacity, and task-launching overhead, the task scheduling over cloud is an NP-hard problem. We propose a low-complexity heuristic algorithm, Max-MCT, to find out the optimal solutions for task scheduling. By estimating the low-bound of finish time, we transform the problem into a virtual knapsack problem. But it is not an optimal solution for the original problem therefore we use a minimal complete time (MCT) algorithm to minimize the entire finish time. We carry out extensive experiments on numerical simulations. The results verified that our algorithm outperforms the existing algorithms.