Models for wireless H.264 video-on-demand services using self-similarity and heavy-tails

Abstract

In this work two video-on-demand (VoD) capacity models for H.264 video traces transmitted using 802.11g are proposed, one based on a self-similar traffic distribution and the other one based in the summation of a large number of Pareto distributed random variables. To ascertain the validity of using such modeling techniques a statistical analysis was performed where it was found that H.264 video traces exhibit self-similarity and heavy-tailed properties, as previous video formats that also use variable bit rate encoding. The models were evaluated against trace based simulations using ns-3 and results from hardware testbeds from other works. The model based on Pareto distributions gives a lower bound on a wide range of buffer sizes, while the model based on self-similarity provides a closer approximation to the user load when buffer size is high. The results show that the models can approximate the maximum user load for H.264 transmission on a local area VoD system and that they depend on the access point buffer size and the desired quality of service expressed as packet-loss probability.