Hurst Parameter Estimation of Long Range Dependent MPEG Video Traffic in ATM Networks

Video is becoming the most important data in asynchronous transfer mode (ATM) networks. In ATM networks, image quality remains almost the same by encoding a video signal at variable bit rates (VBRs). Moving picture experts group (MPEG) video consists of three different frames: intra (I), predictive (P), and bidirectional (B). The important feature of VBR MPEG video traffic is the long-range dependence (LRD) characteristic. To examine the LRD characteristic of real MPEG video sequences, the Hurst parameter is employed. This paper presents a wavelet method, a line length method, and a Fourier filtering method for Hurst parameter estimation and compares their performance of LRD analysis with various video data. The relationship between the Hurst parameter and parameters in fractal modeling is also investigated