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Parallel video transcoding using Hadoop MapReduce

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DOI: 10.23977/jnca.2016.11002 | Downloads: 53 | Views: 2666

Author(s)

Mingang Chen 1, Wenjie Chen 1, Lizhi Cai 1, Zhenyu Liu 1

Affiliation(s)

1 Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai Development Center of Computer Software Technology, Shanghai, 201620, China

Corresponding Author

Mingang Chen

ABSTRACT

Video transcoding has become a key technology for video content distribution network service. In this paper, we propose a novel MapReduce-based parallel video transcoding method. In our method, video files are stored on a shared file system to reduce the overhead of disks I/O and networks in the Hadoop MapReduce. FFmpeg is used to compute the splitting point of the video and the actual video transcoding. Experimental results show that our method can significantly reduce the time of transcoding.

KEYWORDS

video transcoding; Hadoop; MapReduce; FFmpeg; parallel transcoding.

CITE THIS PAPER

Mingang, C. , Wenjie, C. , Zhenyu, L. and Lizhi, C. (2016) Parallel video transcoding using Hadoop MapReduce. Journal of Network Computing and Applications (2016) 1: 7-11.

REFERENCES

[1] Z. H. Li, H. Yan, G. Liu, et al, Cloud transcoder: Bridging the format and resolution gap between internet videos and mobile devices, Proceedings of the 22nd international workshop on Network and Operating System Support for Digital Audio and Video. ACM, 2012: 33-38.
[2] I. Ahmad, X. H. Wei, Y. Sun, et al, Video transcoding: an overview of various techniques and research issues, IEEE Transactions on multimedia, 2005, 7(5): 793-804.
[3] J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters, Communications of the ACM, 2008, 51(1): 107-113.
[4] M. Kim, Y. Cui, S. Han, et al, Towards efficient design and implementation of a Hadoop-based distributed video transcoding system in cloud computing environment, International Journal of Multimedia and Ubiquitous Engineering, 2013, 8(2): 213-224.
[5] C. Ryu, D. Lee, M. Jang, et al, Extensible video processing framework in apache hadoop, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, 2013: 305-310.
[6] A. Garcia, H. Kalva, B. Furht, A study of transcoding on cloud environments for video content delivery, Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing, 2010: 13-18.
[7] FFmpeg website, https://ffmpeg.org/.

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