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Sparse Basis Selection: New Results and Application to Adaptive Prediction of Video Source Traffic
 

Summary: Sparse Basis Selection: New Results and Application to Adaptive
Prediction of Video Source Traffic
Amir F. Atiya
Department of Computer Engineering
Cairo University
Giza, Egypt
Mohamed A. Aly
Department of Computer Engineering
Cairo University
Giza, Egypt
Alexander G. Parlos
Department of Mechanical Engineering
Texas A & M University
College Station, TX 77843
Abstract
Real-time prediction of video source traffic is an important step in many network man-
agement tasks such as dynamic bandwidth allocation and end-to-end quality of service (QoS)
control strategies. In this paper an adaptive prediction model for MPEG-coded traffic is de-
veloped. A novel technology is used, first developed in the signal processing community, called
sparse basis selection. It is based on selecting a small subset of inputs (basis) from among a large

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences