{"title":"Routing Tensor Model with Providing Multimedia Quality","authors":"M. Yevdokymenko","doi":"10.1109/PICST47496.2019.9061280","DOIUrl":null,"url":null,"abstract":"Routing tensor model with providing multimedia quality is proposed. The novelty of the proposed model lies in providing a given level of Quality of Experience regarding the multimedia quality indicator. Due to implementation of the tensornetwork analysis functional, it was possible to provide an analytical calculation of key parameters that influence the multimedia quality level: packet loss probabilities for audio and video flows, as well as the average end-to-end packet delay for the same flows transmitted within the multimedia session. The possibility of analytical and interconnected calculation of these QoS indicators allows to control the impact of desynchronization in time during delivery processes of packets of audio and video flows on multimedia quality, which is significant in the practical implementation of the proposed routing model.","PeriodicalId":6764,"journal":{"name":"2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T)","volume":"22 1","pages":"819-824"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PICST47496.2019.9061280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Routing tensor model with providing multimedia quality is proposed. The novelty of the proposed model lies in providing a given level of Quality of Experience regarding the multimedia quality indicator. Due to implementation of the tensornetwork analysis functional, it was possible to provide an analytical calculation of key parameters that influence the multimedia quality level: packet loss probabilities for audio and video flows, as well as the average end-to-end packet delay for the same flows transmitted within the multimedia session. The possibility of analytical and interconnected calculation of these QoS indicators allows to control the impact of desynchronization in time during delivery processes of packets of audio and video flows on multimedia quality, which is significant in the practical implementation of the proposed routing model.