TY - GEN
T1 - CDVT: A cluster-based distributed video transcoding scheme for mobile stream services
AU - Xu, Cheng
AU - Ren, Wei
AU - Tu, Daxi
AU - Yu, Linchen
AU - Zhu, Tianqing
AU - Ren, Yi
N1 - 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.
PY - 2021/9/2
Y1 - 2021/9/2
N2 - 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%.
AB - 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%.
KW - Distributed transcoding
KW - FFmpeg
KW - Hadoop
KW - Video processing
UR - http://www.scopus.com/inward/record.url?scp=85115348024&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-85928-2_48
DO - 10.1007/978-3-030-85928-2_48
M3 - Conference contribution
AN - SCOPUS:85115348024
SN - 9783030859275
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 608
EP - 628
BT - Wireless Algorithms, Systems, and Applications - 16th International Conference, WASA 2021, Proceedings
A2 - Liu, Zhe
A2 - Wu, Fan
A2 - Das, Sajal K.
PB - Springer
T2 - 16th International Conference on Wireless Algorithms, Systems, and Applications
Y2 - 25 June 2021 through 27 June 2021
ER -