{"title":"Critical bandwidth allocation techniques for stored video delivery across best-effort networks","authors":"W. Feng, Ming Liu","doi":"10.1109/ICDCS.2000.840907","DOIUrl":null,"url":null,"abstract":"We propose two new techniques for the delivery of compressed prerecorded video streams across best-effort networks like the Internet. Current approaches for the delivery of stored video across best-effort networks typically alter the quality of the video frames, the frame rate delivered to the user, or a combination of both. By using network feedback, these algorithms continually adjust the video quality to fit within the available network resources. These approaches, however do not take advantage of the a priori information available from stored video streams, namely the frame sizes that the movie consists of. We show how monitoring the a priori information and actively monitoring a client-side buffer can help smooth the video frame rate delivered to the user, providing a more consistent quality of video.","PeriodicalId":284992,"journal":{"name":"Proceedings 20th IEEE International Conference on Distributed Computing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 20th IEEE International Conference on Distributed Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS.2000.840907","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27
Abstract
We propose two new techniques for the delivery of compressed prerecorded video streams across best-effort networks like the Internet. Current approaches for the delivery of stored video across best-effort networks typically alter the quality of the video frames, the frame rate delivered to the user, or a combination of both. By using network feedback, these algorithms continually adjust the video quality to fit within the available network resources. These approaches, however do not take advantage of the a priori information available from stored video streams, namely the frame sizes that the movie consists of. We show how monitoring the a priori information and actively monitoring a client-side buffer can help smooth the video frame rate delivered to the user, providing a more consistent quality of video.