Sparse basis selection: new results and application to adaptive prediction of video source traffic

• Published in: IEEE transactions on neural networks Vol. 16; no. 5; pp. 1136 - 1146
• Main Authors: Atiya, A.F., Aly, M.A., Parlos, A.G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2005
• Subjects: Adaptive signal processing; Algorithms; Artificial Intelligence; Channel allocation; Communication system traffic control; Computer Simulation; Dictionaries; Forecasting; Information Storage and Retrieval - methods; Internet; Internet traffic; Models, Statistical; MPEG; Pattern Recognition, Automated - methods; Predictive models; Quality management; Quality of service; Signal processing algorithms; Signal Processing, Computer-Assisted; sparse basis; sparse representation; Telecommunications; Traffic control; Video Recording - methods; Video signal processing; video traffic prediction
• ISSN: 1045-9227; 1941-0093
• DOI: 10.1109/TNN.2005.853426

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.