%0 Conference Paper %1 Vega18ITC30 %A Vega, Maria Torres %A Liotta, Antonio %B 30th International Teletraffic Congress (ITC 30) %C Vienna, Austria %D 2018 %K Session_5:_Young_Researchers_Session itc itc30 %T Cognitive Real-Time QoE Management in Video Streaming Services %U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc30/Vega18ITC30.pdf?inline=true %X Satisfying the needs of users of online video streaming services requires not only managing the network Quality of Service (QoS), but also to address the user's Quality of Experience (QoE) expectations. In fact, while QoS factors reflect the status of individual networks they do not comprehensively capture the end-to-end features affecting the quality delivered to the user. In this situation, QoE management is the better option. However, currently used QoE management models are subjective to human interaction and have stringent requirements in terms of time and complexity. Thus, they fail to achieve successful performance in terms of real-timeliness, accuracy, scalability and adaptability. This dissertation work investigates new methods to bring QoE management to the level required by the real-time management of services. In this paper we highlight our main contributions. First, with the aim to perform a combined network-service assessment, we have designed an experimental methodology able to map network QoS onto service QoE. Our methodology is meant to provide service and network providers with the means to pinpoint the working boundaries of their video-sets and to predict the effect of network policies on perception. Second, we have designed and developed a generic machine learning framework that allows deriving accurate predictive No Reference (NR) assessment metrics, based on simplistic NR QoE methods, that are functionally and computationally viable for real-time QoE evaluation. The tools, methods and conclusions derived from this dissertation conform a solid contribution to QoE management of video streaming services, opening new venues for further research and investigation.

@inproceedings{Vega18ITC30, abstract = {Satisfying the needs of users of online video streaming services requires not only managing the network Quality of Service (QoS), but also to address the user's Quality of Experience (QoE) expectations. In fact, while QoS factors reflect the status of individual networks they do not comprehensively capture the end-to-end features affecting the quality delivered to the user. In this situation, QoE management is the better option. However, currently used QoE management models are subjective to human interaction and have stringent requirements in terms of time and complexity. Thus, they fail to achieve successful performance in terms of real-timeliness, accuracy, scalability and adaptability. This dissertation work investigates new methods to bring QoE management to the level required by the real-time management of services. In this paper we highlight our main contributions. First, with the aim to perform a combined network-service assessment, we have designed an experimental methodology able to map network QoS onto service QoE. Our methodology is meant to provide service and network providers with the means to pinpoint the working boundaries of their video-sets and to predict the effect of network policies on perception. Second, we have designed and developed a generic machine learning framework that allows deriving accurate predictive No Reference (NR) assessment metrics, based on simplistic NR QoE methods, that are functionally and computationally viable for real-time QoE evaluation. The tools, methods and conclusions derived from this dissertation conform a solid contribution to QoE management of video streaming services, opening new venues for further research and investigation.}, added-at = {2018-09-12T17:41:00.000+0200}, address = {Vienna, Austria}, author = {Vega, Maria Torres and Liotta, Antonio}, biburl = {https://www.bibsonomy.org/bibtex/274ca0924b47dec6125112b41d65dbd82/itc}, booktitle = {30th International Teletraffic Congress (ITC 30)}, interhash = {385c1dcf6b89e0480c065810be2d8105}, intrahash = {74ca0924b47dec6125112b41d65dbd82}, keywords = {Session_5:_Young_Researchers_Session itc itc30}, timestamp = {2020-05-24T20:14:34.000+0200}, title = {Cognitive Real-Time QoE Management in Video Streaming Services}, url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc30/Vega18ITC30.pdf?inline=true}, year = 2018 }