{"title":"基于gopv的视频流量流体马尔可夫建模","authors":"Wassim Abbessi, H. Nabli","doi":"10.1109/COMNET.2010.5699804","DOIUrl":null,"url":null,"abstract":"The constant evolution of video standards and technologies and the important growth of the amount of video data in networks leads to a need to build new models for video sources. In this paper, we specify a Markovian fluid model based on GoPs (Group of Pictures) that creates a video source descriptor. This descriptor can be used to compute the loss rate observed when transmitting the video via the network. We show also how to build an artificial video traffic having the same statistical characteristics of the original source.","PeriodicalId":444734,"journal":{"name":"The Second International Conference on Communications and Networking","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"GoP-based fluid Markovian modelling of video traffic\",\"authors\":\"Wassim Abbessi, H. Nabli\",\"doi\":\"10.1109/COMNET.2010.5699804\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The constant evolution of video standards and technologies and the important growth of the amount of video data in networks leads to a need to build new models for video sources. In this paper, we specify a Markovian fluid model based on GoPs (Group of Pictures) that creates a video source descriptor. This descriptor can be used to compute the loss rate observed when transmitting the video via the network. We show also how to build an artificial video traffic having the same statistical characteristics of the original source.\",\"PeriodicalId\":444734,\"journal\":{\"name\":\"The Second International Conference on Communications and Networking\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Second International Conference on Communications and Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMNET.2010.5699804\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Second International Conference on Communications and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMNET.2010.5699804","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
摘要
视频标准和技术的不断发展以及网络中视频数据量的重要增长导致需要为视频源建立新的模型。在本文中，我们指定了一个基于GoPs (Group of Pictures)的马尔可夫流体模型来创建视频源描述符。该描述符可用于计算网络传输视频时观察到的损失率。我们还展示了如何建立一个人工视频流量具有相同的统计特征的原始来源。
GoP-based fluid Markovian modelling of video traffic
The constant evolution of video standards and technologies and the important growth of the amount of video data in networks leads to a need to build new models for video sources. In this paper, we specify a Markovian fluid model based on GoPs (Group of Pictures) that creates a video source descriptor. This descriptor can be used to compute the loss rate observed when transmitting the video via the network. We show also how to build an artificial video traffic having the same statistical characteristics of the original source.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
