Ricky K. P. Mok, Edmond W. W. Chan, Xiapu Luo, R. Chang
{"title":"从用户观看活动推断HTTP视频流的QoE","authors":"Ricky K. P. Mok, Edmond W. W. Chan, Xiapu Luo, R. Chang","doi":"10.1145/2018602.2018611","DOIUrl":null,"url":null,"abstract":"HTTP video streaming, employed by most of the video-sharing websites, allows users to control the video playback using, for example, pausing and switching the bit rate. These user-viewing activities can be used to mitigate the temporal structure impairments of the video quality. On the other hand, other activities, such as mouse movement, do not help reduce the impairment level. In this paper, we have performed subjective experiments to analyze user-viewing activities and correlate them with network path performance and user quality of experience. The results show that network measurement alone may miss important information about user dissatisfaction with the video quality. Moreover, video impairments can trigger user-viewing activities, notably pausing and reducing the screen size. By including the pause events into the prediction model, we can increase its explanatory power.","PeriodicalId":387856,"journal":{"name":"W-MUST '11","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"156","resultStr":"{\"title\":\"Inferring the QoE of HTTP video streaming from user-viewing activities\",\"authors\":\"Ricky K. P. Mok, Edmond W. W. Chan, Xiapu Luo, R. Chang\",\"doi\":\"10.1145/2018602.2018611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"HTTP video streaming, employed by most of the video-sharing websites, allows users to control the video playback using, for example, pausing and switching the bit rate. These user-viewing activities can be used to mitigate the temporal structure impairments of the video quality. On the other hand, other activities, such as mouse movement, do not help reduce the impairment level. In this paper, we have performed subjective experiments to analyze user-viewing activities and correlate them with network path performance and user quality of experience. The results show that network measurement alone may miss important information about user dissatisfaction with the video quality. Moreover, video impairments can trigger user-viewing activities, notably pausing and reducing the screen size. By including the pause events into the prediction model, we can increase its explanatory power.\",\"PeriodicalId\":387856,\"journal\":{\"name\":\"W-MUST '11\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"156\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"W-MUST '11\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2018602.2018611\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"W-MUST '11","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2018602.2018611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Inferring the QoE of HTTP video streaming from user-viewing activities
HTTP video streaming, employed by most of the video-sharing websites, allows users to control the video playback using, for example, pausing and switching the bit rate. These user-viewing activities can be used to mitigate the temporal structure impairments of the video quality. On the other hand, other activities, such as mouse movement, do not help reduce the impairment level. In this paper, we have performed subjective experiments to analyze user-viewing activities and correlate them with network path performance and user quality of experience. The results show that network measurement alone may miss important information about user dissatisfaction with the video quality. Moreover, video impairments can trigger user-viewing activities, notably pausing and reducing the screen size. By including the pause events into the prediction model, we can increase its explanatory power.