{"title":"Skype移动视频的网络表征和感知评价","authors":"S. Jana, A. Pande, An Chan, P. Mohapatra","doi":"10.1109/ICCCN.2013.6614157","DOIUrl":null,"url":null,"abstract":"We characterize the performance of both video and network layer properties of Skype, the most popular video telephony application. The performance in both mobile and stationary scenarios is investigated; considering network characteristics such as packet loss, propagation delay, available bandwidth and their effects on the perceptual video quality, measured using spatial and temporal no-reference video metrics. Based on 200+ live traces, we study the performance of this mobile video telephony application. We model video quality as a function of input network parameters and derive a feed-forward Artificial-Neural-Network that accurately predicts video quality given network conditions (0.0206 ≤ MSE ≤ 0.570). The accuracy of this model improves significantly by incorporating end-user mobility as an input to the model.","PeriodicalId":207337,"journal":{"name":"2013 22nd International Conference on Computer Communication and Networks (ICCCN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Network Characterization and Perceptual Evaluation of Skype Mobile Videos\",\"authors\":\"S. Jana, A. Pande, An Chan, P. Mohapatra\",\"doi\":\"10.1109/ICCCN.2013.6614157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We characterize the performance of both video and network layer properties of Skype, the most popular video telephony application. The performance in both mobile and stationary scenarios is investigated; considering network characteristics such as packet loss, propagation delay, available bandwidth and their effects on the perceptual video quality, measured using spatial and temporal no-reference video metrics. Based on 200+ live traces, we study the performance of this mobile video telephony application. We model video quality as a function of input network parameters and derive a feed-forward Artificial-Neural-Network that accurately predicts video quality given network conditions (0.0206 ≤ MSE ≤ 0.570). The accuracy of this model improves significantly by incorporating end-user mobility as an input to the model.\",\"PeriodicalId\":207337,\"journal\":{\"name\":\"2013 22nd International Conference on Computer Communication and Networks (ICCCN)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 22nd International Conference on Computer Communication and Networks (ICCCN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCN.2013.6614157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 22nd International Conference on Computer Communication and Networks (ICCCN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCN.2013.6614157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Network Characterization and Perceptual Evaluation of Skype Mobile Videos
We characterize the performance of both video and network layer properties of Skype, the most popular video telephony application. The performance in both mobile and stationary scenarios is investigated; considering network characteristics such as packet loss, propagation delay, available bandwidth and their effects on the perceptual video quality, measured using spatial and temporal no-reference video metrics. Based on 200+ live traces, we study the performance of this mobile video telephony application. We model video quality as a function of input network parameters and derive a feed-forward Artificial-Neural-Network that accurately predicts video quality given network conditions (0.0206 ≤ MSE ≤ 0.570). The accuracy of this model improves significantly by incorporating end-user mobility as an input to the model.