{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:12:43Z","timestamp":1775365963529,"version":"3.50.1"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research on Frontier Basic Theory and Method of Security Defense for Power Systems with High-dimensional Uncertain Factors","award":["BK20222003"],"award-info":[{"award-number":["BK20222003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972196"],"award-info":[{"award-number":["61972196"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61832008"],"award-info":[{"award-number":["61832008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61832005"],"award-info":[{"award-number":["61832005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization"},{"name":"Sino-German Institutes of Social Computing"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1109\/tmc.2023.3260086","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T20:07:46Z","timestamp":1679429266000},"page":"2422-2437","source":"Crossref","is-referenced-by-count":23,"title":["MetaABR: A Meta-Learning Approach on Adaptative Bitrate Selection for Video Streaming"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9199-3655","authenticated-orcid":false,"given":"Wenzhong","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}]},{"given":"Yeting","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1467-4519","authenticated-orcid":false,"given":"Sanglu","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2013.2281542"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/1943552.1943572"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2398776.2398800"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934898"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098843"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2014.140405"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790118"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2413176.2413189"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2017.2756937"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15986-3_3"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626296"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.2996964"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787486"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2017.2755007"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351014"},{"key":"ref16","first-page":"495","article-title":"Learning in situ: A randomized experiment in video streaming","volume-title":"Proc. 17th USENIX Symp. Networked Syst. Des. Implementation","author":"Yan"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155411"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"ref19","first-page":"18 583","article-title":"Measuring robustness to natural distribution shifts in image classification","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Taori","year":"2020"},{"key":"ref20","first-page":"1","article-title":"Technical report IDSIA","volume":"69\u201396","author":"Schmidhuber","year":"1996"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5529-2_1"},{"key":"ref22","first-page":"10161","article-title":"Model-based adversarial meta-reinforcement learning","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230558"},{"key":"ref24","first-page":"103","article-title":"Swift: Adaptive video streaming with layered neural codecs","volume-title":"Proc. 19th USENIX Symp. Networked Syst. Des. Implementation","author":"Dasari"},{"key":"ref25","first-page":"137","article-title":"YuZu: Neural-enhanced volumetric video streaming","volume-title":"Proc. 19th USENIX Symp. Networked Syst. Des. Implementation","author":"Zhang"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3517027"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3544216.3544228"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3180804"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3544216.3544236"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3538401.3546596"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44668-0_13"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2002.1007449"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"ref34","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3205651.3208249"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau6249"},{"key":"ref37","article-title":"Meta-SGD: Learning to learn quickly for few-shot learning","author":"Li","year":"2017"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/581"},{"key":"ref39","article-title":"A simple neural attentive meta-learner","author":"Mishra","year":"2017"},{"key":"ref40","article-title":"RL$^{2}$2: Fast reinforcement learning via slow reinforcement learning","author":"Duan","year":"2016"},{"key":"ref41","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017"},{"key":"ref42","article-title":"Meta-learning curiosity algorithms","author":"Alet","year":"2020"},{"key":"ref43","article-title":"Discovery of useful questions as auxiliary tasks","author":"Veeriah","year":"2019"},{"key":"ref44","article-title":"On learning intrinsic rewards for policy gradient methods","author":"Zheng","year":"2018"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/icmlc.2004.1382332"},{"key":"ref46","first-page":"5331","article-title":"Efficient off-policy meta-reinforcement learning via probabilistic context variables","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rakelly"},{"key":"ref47","article-title":"Online meta-critic learning for off-policy actor-critic methods","author":"Zhou","year":"2020"},{"key":"ref48","article-title":"Learning to adapt in dynamic, real-world environments through meta-reinforcement learning","author":"Nagabandi","year":"2018"},{"key":"ref49","first-page":"1","article-title":"One-shot imitation learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Duan"},{"key":"ref50","first-page":"1928","article-title":"Asynchronous methods for deep reinforcement learning","volume-title":"Proc. 33rd Int. Conf. Int. Conf. Mach. Learn.","author":"Mnih"},{"key":"ref51","article-title":"Learning to learn: Meta-critic networks for sample efficient learning","author":"Sung","year":"2017"},{"key":"ref52","first-page":"1","article-title":"Meta-reinforcement learning of structured exploration strategies","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Gupta"},{"key":"ref53","article-title":"Efficient off-policy meta-reinforcement learning via probabilistic context variables","author":"Rakelly","year":"2019"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1983.6313077"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2012.2218595"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref57","first-page":"417","article-title":"Mahimahi: Accurate record-and-replay for HTTP","volume-title":"Proc. USENIX Annu. Tech. Conf.","author":"Netravali"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3458306.3458872"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/2483977.2483991"},{"key":"ref60","article-title":"Raw data - measuring broadband America2016","year":"2021"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/2910017.2910618"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107515"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/3339825.3394938"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.7763\/IJMLC.2015.V5.489"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7755\/10422841\/10077780.pdf?arnumber=10077780","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T19:06:45Z","timestamp":1707937605000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10077780\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":64,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2023.3260086","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]}}}