Volume 48 Issue 7
Jul.  2022
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YANG Jingxuan, XU Zhen. Low computational-cost multicast subgrouping for SVC streams[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1278-1286. doi: 10.13700/j.bh.1001-5965.2021.0014(in Chinese)
Citation: YANG Jingxuan, XU Zhen. Low computational-cost multicast subgrouping for SVC streams[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1278-1286. doi: 10.13700/j.bh.1001-5965.2021.0014(in Chinese)

Low computational-cost multicast subgrouping for SVC streams

doi: 10.13700/j.bh.1001-5965.2021.0014
Funds:

National Natural Science Foundation of China 91638301

National Natural Science Foundation of China 91738301

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  • Corresponding author: XU Zhen, E-mail: xuzhen@buaa.edu.cn
  • Received Date: 11 Jan 2021
  • Accepted Date: 26 Mar 2021
  • Publish Date: 09 Apr 2021
  • Multicast is widely used in multimedia transmission because it can utilize channel bandwidth efficiently. However traditional multicast scheme has bottleneck rates problem, which may limit the multicast transmission rate. Subgroup formation can solve the problem, which involves splitting and classifying the multicast group into smaller sub-groups based on intra-group users' channel qualities. Firstly, this paper proposed a user-oriented video multicast transmission scheme, that combined scalable video coding (SVC) technology with subgroup formation. Each subgroup demodulated SVC streams of different layers according to actual data receiving capability, during which the scheme maximized the total system rate on the basis of user video transmission. Moreover, a low computational-cost subgroup algorithm for fair resource allocation is proposed. This proposal considered SVC layer limitation based on low-complexity subgrouping (LCS) algorithm and adopted a constant vector to suppress unfair resource allocation. Finally, the results show that the proposed algorithm achieves well performance in terms of system rate, spectrum efficiency and system fairness when the bandwidth resource and the number of users change. In addition, the computational complexity of the algorithm is low, thus it can be applied to video transmission under 4G and 5G network architectures.

     

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