DocumentCode :
1168648
Title :
Sparse basis selection: new results and application to adaptive prediction of video source traffic
Author :
Atiya, Amir F. ; Aly, Mohamed A. ; Parlos, Alexander G.
Author_Institution :
Dept. of Comput. Eng., Cairo Univ., Giza, Egypt
Volume :
16
Issue :
5
fYear :
2005
Firstpage :
1136
Lastpage :
1146
Abstract :
Real-time prediction of video source traffic is an important step in many network management 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 developed. 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 dictionary of possible inputs. A new sparse basis selection algorithm is developed that is based on efficiently updating the input selection adaptively. When a new measurement is received, the proposed algorithm updates the selected inputs in a recursive manner. Thus, adaptability is not only in the weight adjustment, but also in the dynamic update of the inputs. The algorithm is applied to the problem of single-step-ahead prediction of MPEG-coded video source traffic, and the developed method achieves improved results, as compared to the published results in the literature. The present analysis indicates that the adaptive feature of the developed algorithm seems to add significant overall value.
Keywords :
Internet; bandwidth allocation; prediction theory; quality of service; real-time systems; telecommunication network management; telecommunication traffic; video coding; Internet traffic; MPEG-coded video source traffic; adaptive video source traffic prediction; dynamic bandwidth allocation; end-to-end QoS; network management; quality-of-service; real-time video source traffic prediction; signal processing; sparse basis selection; sparse representation; Adaptive signal processing; Channel allocation; Communication system traffic control; Dictionaries; Predictive models; Quality management; Quality of service; Signal processing algorithms; Traffic control; Video signal processing; Internet traffic; MPEG; sparse basis; sparse representation; video traffic prediction; Algorithms; Artificial Intelligence; Computer Simulation; Forecasting; Information Storage and Retrieval; Internet; Models, Statistical; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Telecommunications; Video Recording;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2005.853426
Filename :
1510715
Link To Document :
بازگشت