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A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing

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  • Wei Zhang

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China & Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan, China)

  • Huiling Shi

    (Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan, China)

  • Xinming Lu

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China)

  • Longquan Zhou

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China)

Abstract

With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system.

Suggested Citation

  • Wei Zhang & Huiling Shi & Xinming Lu & Longquan Zhou, 2018. "A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 13(3), pages 85-98, July.
    • Handle: RePEc:igg:jitwe0:v:13:y:2018:i:3:p:85-98
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