LI Mingzhe, WANG Jinlin, CHEN Xiao, YE Xiaozhou. Memory Copy Optimization for Streaming Gateway Transcoding: Models and Algorithms[J]. Chinese Journal of Electronics, 2016, 25(4): 734-739. doi: 10.1049/cje.2016.06.003
Citation: LI Mingzhe, WANG Jinlin, CHEN Xiao, YE Xiaozhou. Memory Copy Optimization for Streaming Gateway Transcoding: Models and Algorithms[J]. Chinese Journal of Electronics, 2016, 25(4): 734-739. doi: 10.1049/cje.2016.06.003

Memory Copy Optimization for Streaming Gateway Transcoding: Models and Algorithms

doi: 10.1049/cje.2016.06.003
Funds:  This work is supported by the National Key Technology R&D Program (No.2012BAH02B01), and the Key Deployment Project of Chinese Academy of Sciences (No.KGZD-EW-103-4).
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  • Corresponding author: WANG Jinlin (corresponding author) received the B.S. degree in mathematics from University of Science and Technology of China in 1986 and the M.S. degree in acoustics from Institute of Acoustics, Chinese Academy of Sciences (IACAS) in 1989. He is the director and a research fellow in the National Network New Media Engineering Research Center, IACAS. His research interest includes digital signal processing, IP network technology and network streaming media. (
  • Received Date: 2014-06-07
  • Rev Recd Date: 2014-10-11
  • Publish Date: 2016-07-10
  • Repeated memory copy during protocol translation inhibits capacity of a streaming media gateway. Unlike existing optimization techniques that rely on platform-specific features, this paper investigates algorithm-level platform-independent strategies. A mathematical concept of the buf-string is proposed to model the protocol transcoding process. Based on this model three payload extraction algorithms that can reduce memory copy are presented. The streaming gateway used in the Next-generation broadcasting (NGB) and the Next-generation on-demand (NGOD) system is taken as an example to demonstrate and evaluate our strategies. Experimental results from an x86 host and an embedded system prove that our strategies can reduce CPU overhead by 15% to 45%, and optimize the linear space complexity to a constant one.
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