{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:28:13Z","timestamp":1759332493004,"version":"3.41.0"},"reference-count":16,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T00:00:00Z","timestamp":1548374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2019,1,25]]},"abstract":"<jats:p>Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.<\/jats:p>","DOI":"10.1145\/3308897.3308962","type":"journal-article","created":{"date-parts":[[2019,1,28]],"date-time":"2019-01-28T14:01:39Z","timestamp":1548684099000},"page":"155-158","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones"],"prefix":"10.1145","volume":"46","author":[{"given":"Sarah","family":"Wassermann","sequence":"first","affiliation":[{"name":"Inria Paris, Paris , France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolas","family":"Wehner","sequence":"additional","affiliation":[{"name":"University of W\u00fcrzburg &amp; Austrian Institute of Technology, W\u00fcrzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Casas","sequence":"additional","affiliation":[{"name":"Austrian Institute of Technology, Seibersdorf, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,1,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3083187.3083193"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2987443.2987459"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2795176"},{"key":"e_1_2_1_4_1","volume-title":"A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks,\" in EuCNC","author":"Wamser F.","year":"2015","unstructured":"F. Wamser : A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks,\" in EuCNC , 2015 . -, \"YoMoApp: A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks,\" in EuCNC, 2015."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2565585.2565600"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4728-4"},{"key":"e_1_2_1_7_1","volume-title":"Real-time video quality of experience monitoring for HTTPS and QUIC,\" in INFOCOM","author":"Mazhar M. H.","year":"2018","unstructured":"M. H. Mazhar , \" Real-time video quality of experience monitoring for HTTPS and QUIC,\" in INFOCOM , 2018 . M. H. Mazhar et al., \"Real-time video quality of experience monitoring for HTTPS and QUIC,\" in INFOCOM, 2018."},{"key":"e_1_2_1_8_1","volume-title":"Predicting QoE in cellular networks using machine learning and in-smartphone measurements,\" in QoMEX","author":"Casas P.","year":"2017","unstructured":"P. Casas , \" Predicting QoE in cellular networks using machine learning and in-smartphone measurements,\" in QoMEX , 2017 . P. Casas et al., \"Predicting QoE in cellular networks using machine learning and in-smartphone measurements,\" in QoMEX, 2017."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2016.2537645"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2742647.2742670"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1879141.1879173"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2663716.2663726"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1878022.1878030"},{"issue":"164","key":"e_1_2_1_14_1","first-page":"14","article-title":"YouTube QoE Evaluation Tool for Android Wireless Terminals","volume":"2014","author":"G\u00f3mez G.","year":"2014","unstructured":"G. G\u00f3mez , \" YouTube QoE Evaluation Tool for Android Wireless Terminals ,\" EURASIP Journal on Wireless Communications and Networking , vol. 2014 , no. 164 , pp. 1{ 14 , 2014 . G. G\u00f3mez et al., \"YouTube QoE Evaluation Tool for Android Wireless Terminals,\" EURASIP Journal on Wireless Communications and Networking, vol. 2014, no. 164, pp. 1{14, 2014.","journal-title":"EURASIP Journal on Wireless Communications and Networking"},{"key":"e_1_2_1_15_1","volume-title":"Methods for Subjective Determination of Transmission Quality","author":"U-T","year":"1996","unstructured":"International Telecommunication Union, \"IT U-T Recommendation P.800 : Methods for Subjective Determination of Transmission Quality ,\" 1996 . International Telecommunication Union, \"ITU-T Recommendation P.800: Methods for Subjective Determination of Transmission Quality,\" 1996."},{"key":"e_1_2_1_16_1","volume-title":"Parametric Bitstream-based Quality Assessment of Progressive Download and Adaptive Audiovisual Streaming Services over Reliable Transport","author":"U-T","year":"2016","unstructured":"International Telecommunication Union, \"IT U-T Recommendation P.1203 : Parametric Bitstream-based Quality Assessment of Progressive Download and Adaptive Audiovisual Streaming Services over Reliable Transport ,\" 2016 . 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