Abstract
Video traffic model plays an important role in allocating network resources, effectively designing networks, and providing certain quality of service (QoS) to video applications. In this paper a new predictable multifractal model is proposed. Unlike former studies, we investigate the correlation function of multifractal multiplicative process. Based on the lemma we get, we use short-range dependence of coarsest scale coefficient to control the long-range dependence of video trace in multifractal tree. Furthermore, we choose the distribution of multipliers at each time scale by Kullback-Leibler (K-L) distance. Our model (PMFM) has both advantages of AR process and multifractal process. It can be used for traffic prediction while holding the multifractal nature of original video traffic. Simulation shows that our model has better accuracy and stationarity than traditional MFM.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
