CDVT: A cluster-based distributed video transcoding scheme for mobile stream services

Xu, Cheng, Ren, Wei, Tu, Daxi, Yu, Linchen, Zhu, Tianqing and Ren, Yi ORCID: (2021) CDVT: A cluster-based distributed video transcoding scheme for mobile stream services. In: Wireless Algorithms, Systems, and Applications - 16th International Conference, WASA 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer, CHN, pp. 608-628. ISBN 9783030859275

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Distributed video transcoding has been used to huge video data storage overhead and reduce transcoding delay caused by the rapid development of mobile video services. Distributed transcoding can leverage the computing power of clusters for various user requests and diverse video processing demands. However, it imposes a remaining challenge on how to efficiently utilize the computing power of the cluster as well as achieve optimized performance through reasonable system parameters and video processing configurations. In this paper, we design a Cluster-based Distributed Video Transcoding System called CDVT using Hadoop, FFmpeg, and Mkvmerge to achieve on-demand video splitting, on-demand transcoding, and distributed processing, which can be applied to large scale video sharing over mobile devices. In order to further optimize system performance, we conducted extensive experiments on various data sets to find relevant factors that affect transcoding efficiency. We dynamically reconfigure the cluster and evaluate the impacts of different intermediate tasks, splitting strategies, and memory configuration strategies on system performance. Experimental results obtained under various workloads demonstrate that the proposed system can ensure the quality of transcoding tasks while reducing the time cost by up to 50%.

Item Type: Book Section
Additional Information: Funding Information: The research was financially supported by National Natural Science Foundation of China (No. 61972366), the Foundation of Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences (No. KFKT2019-003), Major Scientific and Technological Special Project of Guizhou Province (No. 20183001), and the Foundation of Guizhou Provincial Key Laboratory of Public Big Data (No. 2018BDKFJJ009, No. 2019BDKFJJ003, No. 2019BDKFJJ011). Publisher Copyright: © 2021, Springer Nature Switzerland AG.
Uncontrolled Keywords: distributed transcoding,ffmpeg,hadoop,video processing,theoretical computer science,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: LivePure Connector
Date Deposited: 16 Oct 2021 00:58
Last Modified: 13 Feb 2023 10:30
DOI: 10.1007/978-3-030-85928-2_48


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