A containerized media cloud for video transcoding service

Videos transmitted on the Internet need multiple versions due to the variety of needs from users. Video providers need to transcode an original video into different formats, with different bitrates and resolutions to satisfy those needs. To meet such demands, we design and implement a novel lightweight containerized media cloud for video transcoding service in this paper. We show the high efficiency, robustness, scalability and portability of our system.

[1]  Mou Ling Dennis Wong,et al.  Distributed video transcoding on a heterogeneous computing platform , 2016, 2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS).

[2]  Zhenyu Liu,et al.  Parallel video transcoding using Hadoop MapReduce , 2016 .

[3]  Hui Zhao,et al.  Prediction-Based and Locality-Aware Task Scheduling for Parallelizing Video Transcoding Over Heterogeneous MapReduce Cluster , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Rong Xie,et al.  A lightweight distributed media processing system for UHD service , 2017, 2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB).

[5]  Euiseong Seo,et al.  Extensible Video Processing Framework in Apache Hadoop , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.