The measurement studies show that the burstiness of packet traffic in LAN as well as WAN is associated with self-similar and long-range dependency, and Hurst index is the key value of this model representing the burstiness of traffic. With the analysis in discrete wavelet domain, the nature of the wavelet coefficients and their statistical properties are proposed. Then an adaptive, efficient unbiased estimator of Hurst index based on multiresolution wavelet analysis and weighted regression is presented. Simulation results based on fractal Gaussian noise and real traffic data reveal the proposed approach shows more adaptiveness, accuracy and robustness than traditional estimators which has only O(N) computation. Thus this estimator can be applied to the application of traffic management and real-time control in high-speed networks.