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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 cloudbased 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.

Abstract-The fast emerging cloud services have received a tremendous amount of attention in both industry and academia. Storage services such as Dropbox and iCloud enabled us to share files among multiple users or devices. Providing the benefits of network coding in distributed systems such as Peerto-Peer file sharing and multimedia streaming, researchers have also been trying to apply network coding in storage systems. Existing works have been focusing on mechanisms for preserving the level of redundancy when one or more nodes fail or leave the system. However, file updates, the most frequent operations performed on files, pose challenges in maintaining coded information in the system up to date. In other words, any change in the file will impact all coded blocks in the system, so all traces of the file must be completely replaced. This becomes costly since recomputing coded blocks is very CPU intensive and replacing all the coded blocks consumes an excessive amount of bandwidth. To the best of our knowledge, there has been no work addressing this update problem. To this end, we propose a Differential Update Model (DUM) that will update coded blocks by delivering only the changes in the file. We also present our objective view of the model through a complete analysis on computational complexity and bandwidth saving and simulated experiments.

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