Long-range prediction for real-time MPEG video traffic: An H ∞ filter approach

Chih Hu Wang*, Bor Sen Chen, Bore Kuen Lee, Tsu Tian Lee, Chien-Nan Liu, Chau-Chin Su

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A novel prediction scheme is proposed for real-time MPEG video to predict the burst and long-range dependent traffic. The trend and periodic characteristics of MPEG video traffic are fully captured by a proposed stochastic state-space dynamic model. Then a recursive H filtering algorithm is proposed to estimate traffic for long-range prediction. Simulation results based on real MPEG traffic data show that the proposed scheme has superior performance and lower complexity than some adaptive neural network methods, such as TDNN, NARX, and Elman neural networks.

Original languageEnglish
Article number4625974
Pages (from-to)1771-1775
Number of pages5
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume18
Issue number12
DOIs
StatePublished - 1 Dec 2008

Keywords

  • H filter
  • Long-range dependence
  • MPEG video
  • State-space method

Fingerprint Dive into the research topics of 'Long-range prediction for real-time MPEG video traffic: An H ∞ filter approach'. Together they form a unique fingerprint.

Cite this