{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T20:41:55Z","timestamp":1792269715628,"version":"4.4.0"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"NIH","doi-asserted-by":"publisher","award":["R01EY030470"],"award-info":[{"award-number":["R01EY030470"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"NSF","doi-asserted-by":"publisher","award":["CNS-1763617, CNS-1901137, CNS-2106463,CNS-2102963, CAREER-2045641, CNS-2106299, CCF-2031881, ECCS-2032387, CNS-2040088, CNS-2032033, CNS-2106150"],"award-info":[{"award-number":["CNS-1763617, CNS-1901137, CNS-2106463,CNS-2102963, CAREER-2045641, CNS-2106299, CCF-2031881, ECCS-2032387, CNS-2040088, CNS-2032033, CNS-2106150"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2024,5,21]]},"abstract":"<jats:p>Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth. Recent advances in neural enhancement have shown promising results in improving the quality of degraded videos through deep learning. Neural-Enhanced Streaming (NES) incorporates this new approach into video streaming, allowing users to download low-quality video segments and then enhance them to obtain high-quality content without violating the playback of the video stream. We introduce BONES, an NES control algorithm that jointly manages the network and computational resources to maximize the quality of experience (QoE) of the user. BONES formulates NES as a Lyapunov optimization problem and solves it in an online manner with near-optimal performance, making it the first NES algorithm to provide a theoretical performance guarantee. Comprehensive experimental results indicate that BONES increases QoE by 5% to 20% over state-of-the-art algorithms with minimal overhead. Our code is available at https:\/\/github.com\/UMass-LIDS\/bones.<\/jats:p>","DOI":"10.1145\/3656014","type":"journal-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T10:40:32Z","timestamp":1716979232000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["BONES: Near-Optimal Neural-Enhanced Video Streaming"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4672-2243","authenticated-orcid":false,"given":"Lingdong","family":"Wang","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0867-2528","authenticated-orcid":false,"given":"Simran","family":"Singh","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, Newark, New Jersey, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2428-9518","authenticated-orcid":false,"given":"Jacob","family":"Chakareski","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technolog, Newark, New Jersey, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9278-2254","authenticated-orcid":false,"given":"Mohammad","family":"Hajiesmaili","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0558-6875","authenticated-orcid":false,"given":"Ramesh K.","family":"Sitaraman","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2015.7351097"},{"key":"e_1_2_1_2_1","volume-title":"NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.","author":"Agustsson Eirikur","year":"2017","unstructured":"Eirikur Agustsson and Radu Timofte. 2017. NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230558"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3229079"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592473.3592569"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2005.854227"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2006.886299"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544494"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2022.3151612"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00491"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00588"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386290.3396929"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00235"},{"key":"e_1_2_1_14_1","volume-title":"Raw Data - Measuring Broadband America","author":"Federal Communications Commission","year":"2016","unstructured":"Federal Communications Commission. 2016. Raw Data - Measuring Broadband America 2016. https:\/\/www.fcc.gov\/reports-research\/reports\/measuring-broadband-america\/raw-data-measuring-broadband-america-2016 https:\/\/www.fcc.gov\/reports-research\/reports\/measuring-broadband-america\/raw-data-measuring-broadband-america-2016."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155477"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--1--4615--2329--1"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3228521"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3301293.3302373"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2740070.2626296"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00768"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351084"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2413176.2413189"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387514.3405856"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00730"},{"key":"e_1_2_1_25_1","volume-title":"Computer Vision -- ECCV","author":"Li Xiaoqi","year":"2022","unstructured":"Xiaoqi Li, Jiaming Liu, Shizun Wang, Cheng Lyu, Ming Lu, Yurong Chen, Anbang Yao, Yandong Guo, and Shanghang Zhang. 2022. Efficient Meta-Tuning for\u00a0Content-Aware Neural Video Delivery. In Computer Vision -- ECCV 2022, , Shai Avidan, Gabriel Brostow, Moustapha Ciss\u00e9 , Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer Nature Switzerland, Cham, 308--324."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.151"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3558261"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098843"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491056"},{"key":"e_1_2_1_31_1","volume-title":"Stochastic Network Optimization with Application to Communication and Queueing Systems","author":"Neely M.","unstructured":"M. Neely. 2010. Stochastic Network Optimization with Application to Communication and Queueing Systems. Morgan & Claypool Publishers. https:\/\/books.google.com\/books?id=sZpeAQAAQBAJ"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2012.2204831"},{"key":"e_1_2_1_33_1","unstructured":"NVIDIA. 2023. Video Processing Framework. https:\/\/github.com\/NVIDIA\/VideoProcessingFramework"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2483977.2483991"},{"key":"e_1_2_1_35_1","unstructured":"Sandvine. 2023. 2023 Global Internet Phenomena Report. https:\/\/www.sandvine.com\/global-internet-phenomena-report-2023 https:\/\/www.sandvine.com\/global-internet-phenomena-report-2023."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547750"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBC.2021.3131875"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336497"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.2996964"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1943552.1943572"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00323"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00143"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2011.2111364"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2016.2601087"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534088.3534344"},{"key":"e_1_2_1_46_1","volume-title":"17th USENIX Symposium on Networked Systems Design and Implementation (NSDI '20)","author":"Yan Francis Y.","year":"1866","unstructured":"Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein. [n.,d.]. Learning in situ: a randomized experiment in video streaming. 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI '20) ( [n.,d.]). https:\/\/par.nsf.gov\/biblio\/10186616"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419185"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.5555\/3291168.3291216"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544216.3544218"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2829988.2787486"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00295"},{"key":"e_1_2_1_52_1","unstructured":"Zijie Yue and Miaojing Shi. 2023. Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction. arxiv: 2207.08960 [cs.CV]"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_31"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBC.2021.3071010"},{"key":"e_1_2_1_55_1","volume-title":"YuZu: Neural-Enhanced Volumetric Video Streaming. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)","author":"Zhang Anlan","year":"2022","unstructured":"Anlan Zhang, Chendong Wang, Bo Han, and Feng Qian. 2022. YuZu: Neural-Enhanced Volumetric Video Streaming. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22). USENIX Association, Renton, WA, 137--154. https:\/\/www.usenix.org\/conference\/nsdi22\/presentation\/zhang-anlan"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155384"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBC.2022.3227419"}],"container-title":["Proceedings of the ACM on Measurement and Analysis of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3656014","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3656014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T18:02:18Z","timestamp":1755972138000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3656014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,21]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,5,21]]}},"alternative-id":["10.1145\/3656014"],"URL":"https:\/\/doi.org\/10.1145\/3656014","relation":{},"ISSN":["2476-1249"],"issn-type":[{"value":"2476-1249","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,21]]},"assertion":[{"value":"2024-05-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}