Publication details

 

Integer Programming for Media Streams Planning Problem

Basic information
Original title:Integer Programming for Media Streams Planning Problem
Authors:Pavel Troubil, Hana Rudová
Further information
Citation:TROUBIL, Pavel a Hana RUDOVÁ. Integer Programming for Media Streams Planning Problem. In Luděk Matyska and Michal Kozubek and Tomáš Vojnar and Pavel Zemčík and David Antoš. Sixth Doctoral Workshop on Mathematical and Engineering Methods in Computer Science (MEMICS'10) -- Selected Papers. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2011. s. 116--123, 9 s. ISBN 978-3-939897-22-4.Export BibTeX
@inproceedings{935725,
author = {Troubil, Pavel and Rudová, Hana},
address = {Dagstuhl, Germany},
booktitle = {Sixth Doctoral Workshop on Mathematical and Engineering Methods in Computer Science (MEMICS'10) -- Selected Papers},
editor = {Luděk Matyska and Michal Kozubek and Tomáš Vojnar and Pavel Zemčík and David Antoš},
keywords = {Media streams planning; integer programming},
language = {eng},
location = {Dagstuhl, Germany},
isbn = {978-3-939897-22-4},
pages = {116--123},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
title = {Integer Programming for Media Streams Planning Problem},
url = {http://drops.dagstuhl.de/opus/volltexte/2011/3066},
year = {2011}
}
Original language:English
Field:Informatics
WWW:link to a new windowhttp://drops.dagstuhl.de/opus/volltexte/2011/3066
Type:Article in Proceedings
Keywords:Media streams planning; integer programming

Continually increasing demands for high-quality videoconferencing have brought a problem of fully automated environment setup. A media streams planning problem forms an important part of this issue. As the multimedia streams are extremely bandwidth-demanding, their transmission has to be planned with respect to available capacities of network links and the plan also needs to be optimal in terms of data transfer latencies. This paper presents an integer programming solution of the problem and its implementation. The implementation achieved very promising results in performance-evaluating measurements. Compared to previous constraint-based solver, it is capable of finding optimal solution significantly faster, allowing for real-time planning of larger problem instances.

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