Dynamic bandwidth allocation for VBR video traffic using adaptive wavelet prediction

Dynamic bandwidth allocation using adaptive prediction can significantly improve the efficiency and QoS guarantees in transporting VBR video over an ATM network. Conventionally, the time-domain least-mean-square (LMS) predictor is used, with the drawback of slow convergence. In VBR video traffic characterized by frequent scene changes, this slow convergence may result in extended periods of intractability and excessive cell loss during scene changes. We propose an adaptive wavelet predictor for dynamic bandwidth allocation. The wavelet predictor converges faster and hence, tracks scene changes better. Our simulation results show that, in comparison with the LMS predictor, the wavelet predictor reduces the prediction error by an average of 11% over the six half-an-hour-long empirical MPEG-1 traces. The dynamic bandwidth allocation using wavelet prediction significantly reduces the cell-loss-rate over various network settings, especially at large buffer sizes