The Statistical Analysis of the Live TV Bit Rate

TL;DR

This study analyzes the statistical distribution of live TV streaming bit rates to optimize bandwidth allocation, revealing that generalized extreme and tlocationscale distributions best fit the data, aiding in traffic management and server scheduling.

Contribution

The paper identifies the best-fit statistical distributions for live TV streaming bit rates, providing parameters for improved server scheduling and traffic engineering.

Findings

Generalized extreme distribution fits most channels best.

Tlocationscale distribution fits the entire system.

Results aid in designing better streaming server policies.

Abstract

This paper studies the statistical nature of TV channels streaming variable bit rate distribution and allocation. The goal of the paper is to derive the best-fit rate distribution to describe TV streaming bandwidth allocation, which can reveal traffic demands of users. Our analysis uses multiplexers channel bandwidth allocation (PID) data of 13 TV live channels. We apply 17 continuous and 3 discrete distributions to determine the best-fit distribution function for each individual channel and for the whole set of channels. We found that the generalized extreme distribution fitting most of our channels most precisely according to the Bayesian information criterion. By the same criterion tlocationscale distribution matches best for the whole system. We use these results to propose parameters for streaming server queuing model. Results are useful for streaming servers scheduling policy design process targeting to improve limited infrastructural resources, traffic engineering through dynamic routing at CDN, SDN.