Analysis of real-time multimedia traffic in the context of self-similarity

Multimedia networks are now providing versatile services to meet different types of subscriber needs including voice, video, data, etc. Voice and video conferencing applications over these IP networks have already gained wide acceptance in todaypsilas end-user communities. The traffic characteristics and intensity as a function of time, geographic source can be realized by observing traffic distribution. Multimedia traffic has characteristics very different from Poisson characteristics, generating high rate data at one time and low rate data at another. Variable bit rate (VBR) real-time applications such as compressed video and audio and also the data sources tend to generate bursty traffic patterns and exhibit certain degree of correlation between arrivals and show long-range dependence in time i.e. self-similarity [1, 2, 3, 4, 5]. Therefore, it is important to identify and characterize network traffic flows to analyze network performance. In the present work the analysis of real time audio and video streaming traffic, colleted from the videoconferencing session of an enterprise high-speed hybrid multimedia network, has been done in the context of self-similarity.