Fuzzy neural network for VBR MPEG video traffic prediction

  • Authors:

  • Xiaoying Liu;Xiaodong Liu;Xiaokang Lin;Qionghai Dai

  • Affiliations:

  • Research Center of Broadband Network & Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, China;Research Center of Broadband Network & Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, China;Research Center of Broadband Network & Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, China;Research Center of Broadband Network & Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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Abstract

As a main video transmission mode for digital media networks, the capability to predict VBR video traffic can significantly improve the effectiveness of quality of services. Therefore, based on fuzzy theory and neural network, a novel video traffic prediction model is proposed for the complex traffic characteristics of MPEG videos. This model reduces the prediction error and computation. Simulation results show that the proposed method is able to predict the original traffic more accurately and reliably than the conventional AR method.