Neural Networks
On-line prediction of nonstationary variable-bit-rate video traffic
IEEE Transactions on Signal Processing
Feed Forward Bandwidth Indication (FFBI): Cooperation for an accurate bandwidth forecast
Computer Communications
Target tracking and positioning on video sequence from a moving video camera
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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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.