The Statistical Analysis of the Live TV Bit Rate
This work is incremental, providing statistical models to improve streaming server scheduling and traffic engineering for limited infrastructure resources.
The paper tackled the problem of modeling TV channel streaming bit rate distributions to understand user traffic demands, finding that the generalized extreme distribution best fits individual channels and the tlocationscale distribution fits the whole system based on data from 13 channels.
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.