Enhancing QoE Based on Machine Learning and DASH in SDN Networks. Abar, T., Letaifa, A. B., & Elasmi, S. In 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pages 258–263, May, 2018. 00000doi abstract bibtex In recent years, networks have become an important channel for the distribution of multimedia data, mainly via the HTTP protocol. Several intelligent streaming protocols have been based on the HTTP protocol to achieve smooth, high-quality streaming and a better Quality of Experience (QoE). Among these protocols, there is the latest and the newest international standard MPEG DASH. This technique introduces an additional level of complexity for measuring perceived video quality, as it varies the video bit rate. This work adopts an SDN-based architecture framework that aims to optimize the QoE for video streaming in SDN networks using DASH protocol whilst also taking into account the variety of devices, video parameters and the network requirements. We try to model the optimization problem of QoE based on several parameters that effect the user perception such as stall number, bitrates ... Our module is composed of two phases: estimation phase of available resources based on Machine Learning, adaptation and selection phase based on the results of the first one.
@inproceedings{abar_enhancing_2018,
title = {Enhancing {QoE} {Based} on {Machine} {Learning} and {DASH} in {SDN} {Networks}},
doi = {10.1109/WAINA.2018.00095},
abstract = {In recent years, networks have become an important channel for the distribution of multimedia data, mainly via the HTTP protocol. Several intelligent streaming protocols have been based on the HTTP protocol to achieve smooth, high-quality streaming and a better Quality of Experience (QoE). Among these protocols, there is the latest and the newest international standard MPEG DASH. This technique introduces an additional level of complexity for measuring perceived video quality, as it varies the video bit rate. This work adopts an SDN-based architecture framework that aims to optimize the QoE for video streaming in SDN networks using DASH protocol whilst also taking into account the variety of devices, video parameters and the network requirements. We try to model the optimization problem of QoE based on several parameters that effect the user perception such as stall number, bitrates ... Our module is composed of two phases: estimation phase of available resources based on Machine Learning, adaptation and selection phase based on the results of the first one.},
booktitle = {2018 32nd {International} {Conference} on {Advanced} {Information} {Networking} and {Applications} {Workshops} ({WAINA})},
author = {Abar, T. and Letaifa, A. Ben and Elasmi, S.},
month = may,
year = {2018},
note = {00000},
pages = {258--263},
file = {Abar et al_2018_Enhancing QoE Based on Machine Learning and DASH in SDN Networks.pdf:/home/alan/snap/zotero-snap/10/Zotero/storage/2XHB8EGT/Abar et al_2018_Enhancing QoE Based on Machine Learning and DASH in SDN Networks.pdf:application/pdf}
}
Downloads: 0
{"_id":"8nbDZY765oML3o6EB","bibbaseid":"abar-letaifa-elasmi-enhancingqoebasedonmachinelearninganddashinsdnnetworks-2018","authorIDs":[],"author_short":["Abar, T.","Letaifa, A. B.","Elasmi, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Enhancing QoE Based on Machine Learning and DASH in SDN Networks","doi":"10.1109/WAINA.2018.00095","abstract":"In recent years, networks have become an important channel for the distribution of multimedia data, mainly via the HTTP protocol. Several intelligent streaming protocols have been based on the HTTP protocol to achieve smooth, high-quality streaming and a better Quality of Experience (QoE). Among these protocols, there is the latest and the newest international standard MPEG DASH. This technique introduces an additional level of complexity for measuring perceived video quality, as it varies the video bit rate. This work adopts an SDN-based architecture framework that aims to optimize the QoE for video streaming in SDN networks using DASH protocol whilst also taking into account the variety of devices, video parameters and the network requirements. We try to model the optimization problem of QoE based on several parameters that effect the user perception such as stall number, bitrates ... Our module is composed of two phases: estimation phase of available resources based on Machine Learning, adaptation and selection phase based on the results of the first one.","booktitle":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","author":[{"propositions":[],"lastnames":["Abar"],"firstnames":["T."],"suffixes":[]},{"propositions":[],"lastnames":["Letaifa"],"firstnames":["A.","Ben"],"suffixes":[]},{"propositions":[],"lastnames":["Elasmi"],"firstnames":["S."],"suffixes":[]}],"month":"May","year":"2018","note":"00000","pages":"258–263","file":"Abar et al_2018_Enhancing QoE Based on Machine Learning and DASH in SDN Networks.pdf:/home/alan/snap/zotero-snap/10/Zotero/storage/2XHB8EGT/Abar et al_2018_Enhancing QoE Based on Machine Learning and DASH in SDN Networks.pdf:application/pdf","bibtex":"@inproceedings{abar_enhancing_2018,\n\ttitle = {Enhancing {QoE} {Based} on {Machine} {Learning} and {DASH} in {SDN} {Networks}},\n\tdoi = {10.1109/WAINA.2018.00095},\n\tabstract = {In recent years, networks have become an important channel for the distribution of multimedia data, mainly via the HTTP protocol. Several intelligent streaming protocols have been based on the HTTP protocol to achieve smooth, high-quality streaming and a better Quality of Experience (QoE). Among these protocols, there is the latest and the newest international standard MPEG DASH. This technique introduces an additional level of complexity for measuring perceived video quality, as it varies the video bit rate. This work adopts an SDN-based architecture framework that aims to optimize the QoE for video streaming in SDN networks using DASH protocol whilst also taking into account the variety of devices, video parameters and the network requirements. We try to model the optimization problem of QoE based on several parameters that effect the user perception such as stall number, bitrates ... Our module is composed of two phases: estimation phase of available resources based on Machine Learning, adaptation and selection phase based on the results of the first one.},\n\tbooktitle = {2018 32nd {International} {Conference} on {Advanced} {Information} {Networking} and {Applications} {Workshops} ({WAINA})},\n\tauthor = {Abar, T. and Letaifa, A. Ben and Elasmi, S.},\n\tmonth = may,\n\tyear = {2018},\n\tnote = {00000},\n\tpages = {258--263},\n\tfile = {Abar et al_2018_Enhancing QoE Based on Machine Learning and DASH in SDN Networks.pdf:/home/alan/snap/zotero-snap/10/Zotero/storage/2XHB8EGT/Abar et al_2018_Enhancing QoE Based on Machine Learning and DASH in SDN Networks.pdf:application/pdf}\n}\n\n","author_short":["Abar, T.","Letaifa, A. B.","Elasmi, S."],"key":"abar_enhancing_2018","id":"abar_enhancing_2018","bibbaseid":"abar-letaifa-elasmi-enhancingqoebasedonmachinelearninganddashinsdnnetworks-2018","role":"author","urls":{},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"http://www.telemidia.puc-rio.br/~alan/files/all.bib","creationDate":"2020-03-03T14:08:15.106Z","downloads":0,"keywords":[],"search_terms":["enhancing","qoe","based","machine","learning","dash","sdn","networks","abar","letaifa","elasmi"],"title":"Enhancing QoE Based on Machine Learning and DASH in SDN Networks","year":2018,"dataSources":["jAxurbvLP8q5LTdLa"]}