Dynamic resource allocation via video content and short-term traffic statistics

The reliable and efficient transmission of high-quality variable bit rate (VBR) video through the Internet generally requires network resources be allocated in a dynamic fashion. This includes the determination of when to renegotiate for network resources, as well as how much to request at a given time. The accuracy of any resource request method depends critically on its prediction of future traffic patterns. Such a prediction can be performed using the content and traffic information of short video segments. This paper presents a systematic approach to select the best features for prediction, indicating that while content is important in predicting the bandwidth of a video hit stream, the use of both content and available short-term bandwidth statistics can yield significant improvements. A new framework for traffic prediction is proposed in this paper; experimental results show a smaller mean-square resource prediction error and higher overall link utilization.

[1]  D. Woolley.  The White Paper. , 1972, British medical journal.

[2]  Jean C. Walrand.  Communication networks - a first course , 1991 .

[3]  Ramesh Jain,et al.  Storage and Retrieval for Still Image and Video Databases IV , 1996 .

[4]  Michael R. Izquierdo,et al.  A survey of statistical source models for variable-bit-rate compressed video , 1999, Multimedia Systems.

[5]  Edward W. Knightly,et al.  RED-VBR: A New Approach to Support Delay-Sensitive VBR Video in Packet-Switched Networks , 1995, NOSSDAV.

[6]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[7]  Mohammed Ghanbari,et al.  MPEG video modelling based on scene description , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[8]  John S. Boreczky,et al.  Comparison of video shot boundary detection techniques , 1996, J. Electronic Imaging.

[9]  Shih-Fu Chang,et al.  Principles and applications of content-aware video communication , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[10]  Edward W. Knightly,et al.  D-BIND: an accurate traffic model for providing QoS guarantees to VBR traffic , 1997, TNET.

[11]  Tony R. Martinez,et al.  Digital Neural Networks , 1988, Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics.

[12]  San-qi Li,et al.  Predictive Dynamic Bandwidth Allocation for Efficient Transport of Real-Time VBR Video over ATM , 1995, IEEE J. Sel. Areas Commun..

[13]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[14]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[15]  J. Wade Davis,et al.  Statistical Pattern Recognition , 2003, Technometrics.

[16]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[17]  J. Kittler,et al.  Feature Set Search Alborithms , 1978 .

[18]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[21]  Uwe Hartmann,et al.  Mapping neural network derived from the parzen window estimator , 1992, Neural Networks.

[22]  Boon-Lock Yeo,et al.  Rapid scene analysis on compressed video , 1995, IEEE Trans. Circuits Syst. Video Technol..