%0 Conference Paper %1 info3-article-2019-9 %A Orsolic, Irena %A Skorin-Kapov, Lea %A Hoßfeld, Tobias %B 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) (QoMEX 2019) %D 2019 %K myown QoE YouTube encrypted learning machine monitoring traffic %T To Share or Not to Share? How Exploitation of Context Data Can Improve In-Network QoE Monitoring of Encrypted YouTube Streams %X With the widespread use of encryption in Over the Top (OTT) traffic, Internet Service Providers (ISPs) for the most part lack insights into application performance, as well as into Quality of Experience (QoE) perceived by end users. Addressing challenges related to encryption, ISPs are looking into machine learning based solutions that can detect application performance solely from statistical properties of the traffic. On the other hand, OTT service providers are not willing to share service performance and content-related information with ISPs. While related work on OTT-ISP collaboration scenarios has addressed architectural aspects, business models, and to a certain extent incentives for sharing data, the focus of this paper is on the exchanged data itself. We investigate to what extent the performance of in-network machine learning based QoE estimation models for HTTP adaptive video streaming could be improved with the availability of certain context data provided by OTT providers. We motivate OTT-ISP collaboration through more accurate in-network QoE monitoring and potential improvement of user experience, which is of interest to both sides.

@inproceedings{info3-article-2019-9, abstract = {With the widespread use of encryption in Over the Top (OTT) traffic, Internet Service Providers (ISPs) for the most part lack insights into application performance, as well as into Quality of Experience (QoE) perceived by end users. Addressing challenges related to encryption, ISPs are looking into machine learning based solutions that can detect application performance solely from statistical properties of the traffic. On the other hand, OTT service providers are not willing to share service performance and content-related information with ISPs. While related work on OTT-ISP collaboration scenarios has addressed architectural aspects, business models, and to a certain extent incentives for sharing data, the focus of this paper is on the exchanged data itself. We investigate to what extent the performance of in-network machine learning based QoE estimation models for HTTP adaptive video streaming could be improved with the availability of certain context data provided by OTT providers. We motivate OTT-ISP collaboration through more accurate in-network QoE monitoring and potential improvement of user experience, which is of interest to both sides.}, added-at = {2019-08-14T15:51:07.000+0200}, author = {Orsolic, Irena and Skorin-Kapov, Lea and Hoßfeld, Tobias}, biburl = {https://www.bibsonomy.org/bibtex/2528fab7c753fa512865280e84836fa48/uniwue_info3}, booktitle = {2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) (QoMEX 2019)}, interhash = {d17c281adb6c7965658e1621ecb45894}, intrahash = {528fab7c753fa512865280e84836fa48}, keywords = {myown QoE YouTube encrypted learning machine monitoring traffic}, month = {6}, timestamp = {2022-03-14T00:08:42.000+0100}, title = {To Share or Not to Share? How Exploitation of Context Data Can Improve In-Network QoE Monitoring of Encrypted YouTube Streams}, year = 2019 }