Dependency-aware distributed video transcoding in the cloud

To improve the quality of experience of video streaming services, content providers are challenged by the need to prepare videos at different quality levels appropriate to the network infrastructure and device hardware specification. Distributed video transcoding in the cloud has received many research attentions to address this challenge. Such a cloud-based solution segments a video into multiple video chunks and distributes chunks to virtual machines in the cloud for parallel transcoding. However, by inspecting video codec standards, we learn that important inter-dependency among video frames is broken if the video is segmented into fixed-size chunks, which leads to increasing bitrate and transcoding time. In this paper, we propose a distributed video transcoding scheme that exploits dependency among GOPs by preparing video chunks of variable size. Experimental results from real video sequences with diverse visual features show that the proposed transcoding scheme effectively reduces bitrate and transcoding time.