Analysis, modeling and generation of self-similar VBR video traffic

TLDR

The study analyzes a 2‑hour VBR video sample and develops a non‑Markovian source model with an algorithm to generate synthetic traffic. They examined a 2‑hour VBR video obtained by intraframe compression of an action movie, then built a non‑Markovian source model and algorithm to generate synthetic traffic. The analysis shows heavy‑tailed bandwidth distributions and hyperbolic autocorrelation (long‑range dependence), and simulations demonstrate that incorporating these properties yields significant bandwidth efficiency and reveals gaps in existing VBR models.

Abstract

We present a detailed statistical analysis of a 2-hour long empirical sample of VBR video. The sample was obtained by applying a simple intraframe video compression code to an action movie. The main findings of our analysis are (1) the tail behavior of the marginal bandwidth distribution can be accurately described using “heavy-tailed” distributions (e.g., Pareto); (2) the autocorrelation of the VBR video sequence decays hyperbolically (equivalent to long-range dependence ) and can be modeled using self-similar processes. We combine our findings in a new (non-Markovian) source model for VBR video and present an algorithm for generating synthetic traffic. Trace-driven simulations show that statistical multiplexing results in significant bandwidth efficiency even when long-range dependence is present. Simulations of our source model show long-range dependence and heavy-tailed marginals to be important components which are not accounted for in currently used VBR video traffic models.