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Accelerating video encoding using cluster computing
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DOI：10.1007/s11042-020-08717-9.png)
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Abstract
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The great advance and variety of multimedia applications such as video streaming, TV broadcasting, and video conferencing stimulated research to enhance video encoding, where a video is reduced in size and possibly transformed to numerous formats for portability. This paper is concerned with solving the problem of the huge processing time taken by the serial video encoding approaches by proposing a hybrid-parallel video encoding technique to speed up the process. In this work, the Joint Scalable Video Model (JSVM 9.19.14) is chosen as the basic serial video encoding algorithm for building different parallel video encoding architectures. The proposed technique exploits the triple-step nature of JSVM and intelligently determines the best task organization to achieve speedup and increase the efficiency on a cluster computing platform. Moreover, a dynamic load sharing scheme is proposed to redistribute load among different machines for additional parallelism. The remarkable feature of our approach is that, both the granularity of load partitioning among the cluster machines and all the associated overheads are considered. The experimental results are applied on a compact library of 160 mp4 encoded videos and two other bench mark datasets. The results proves a significant improvement in performance in comparison to the sequential version; which ranges from 64.2% to 95.3%, for a cluster with a number of machines ranging from 2 to 20 respectively.
Keywords:
Cluster computing
Parallel computing
Video encoding
Video on demand
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