Towards Efficient Design and Implementation of a Hadoop-based Distributed Video Transcoding System in Cloud Computing Environment

In this paper, we propose a Hadoop-based Distributed Video Transcoding System in a cloud computing environment that transcodes various video codec formats into the MPEG-4 video format. This system provides various types of video content to heterogeneous devices such as smart phones, personal computers, television, and pads. We design and implement the system using the MapReduce framework, which runs on a Hadoop Distributed File System platform, and the media processing library Xuggler. Thus, the encoding time to transcode large amounts of video content is exponentially reduced, facilitating a transcoding function. For performance evaluation, we focus on measuring the total time to transcode a data set into a target data set for three sets of experiments. We also analyze the experimental results, providing optimal Hadoop Distributed File System and MapReduce options suitable for video transcoding. Based on the experiments performed on a 28-node cluster, the proposed distributed video transcoding system provides excellent performance in terms of speed and quality.