{"title":"Distributed adaptation algorithms for rate-controlled video multicast over shared infrastructure networks","authors":"M. Rabby, K. Ravindran, Jun Wu","doi":"10.1109/COMSNETS.2010.5431985","DOIUrl":null,"url":null,"abstract":"We consider a wide-area video conferencing application where the video sources can adapt their send rates according to the available bandwidth in the network paths. We advocate a joint rate control of the sources to relieve the congestion, instead of running multiple instances of a single-source adaptation algorithm and additively superposing their results. The existing techniques work nicely with single-source trees, but do not work optimally in the case of multi-source trees with shared QoS goals. Using the well-known AIMD-based adaptation procedures, we incorporate the topology inferencing mechanism into a coordinated rate adaptation algorithm executed by the loss-experiencing sources. The paper provides a simulation based evaluation of our algorithm to corroborate the benefits.","PeriodicalId":369006,"journal":{"name":"2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMSNETS.2010.5431985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
We consider a wide-area video conferencing application where the video sources can adapt their send rates according to the available bandwidth in the network paths. We advocate a joint rate control of the sources to relieve the congestion, instead of running multiple instances of a single-source adaptation algorithm and additively superposing their results. The existing techniques work nicely with single-source trees, but do not work optimally in the case of multi-source trees with shared QoS goals. Using the well-known AIMD-based adaptation procedures, we incorporate the topology inferencing mechanism into a coordinated rate adaptation algorithm executed by the loss-experiencing sources. The paper provides a simulation based evaluation of our algorithm to corroborate the benefits.