On zap time minimization in IPTV networks

Abstract

Digital television systems have a clear disadvantage relative to analog systems in users' quality of experience, most notably in the time required to change channels, or zap time. The goal of this research is to improve the performance of a multicasting IPTV network, both in user experience and in resource consumption. We formulate the problem of assigning IPTV clients to servers as an integer programming model, in variants which minimize channel-change times, overall network capacity consumption, or both. This problem is shown to be computationally hard, and the performance of the models is tested on problems of different sizes. Polynomial-time heuristics are presented which address a relaxed version of the problem, and the performance of these heuristics is measured.

Multi-source IPTV networks: Zap time and bandwidth optimization

“…The goal of this work is to extend previous work in clientchannel-server assignment by Long et al [5]. Two major differences between this research and previous work are as follows.…”

“…Due to the space limitations, we do not provide the extended IP models for the variants in [5] and only consider the two new variants which focus on residual bandwidth.…”

“…The problem variants discussed in [5] are: Minimize total bandwidth consumption (CAP-MB); minimize maximum zap time (CAP-MZT); minimize maximum zap time, constrain bandwidth (CAP-MZCB) and minimize bandwidth, constraint zap time (CAP-MBCZ). CAP is an offline model and assumes that all channel demands are known in advance, are unordered and that there is no distribution of channels in the network.…”

“…Related works are reviewed in Section II. We briefly describe the work by Long et al [5] and our extension in Section III. IP formulations and heuristic algorithms are introduced in Section IV and Section V respectively.…”

“…By considering the residual bandwidth rather than load distribution we are able to model this problem using Linear Integer Programming rather than the more computationally intensive Nonlinear Integer Programming. We extend the Integer Programming (IP) model in [5] and develop new heuristics for this problem.…”