{"id":"https://openalex.org/W4379469712","doi":"https://doi.org/10.1145/3618257.3624828","title":"Estimating WebRTC Video QoE Metrics Without Using Application Headers","display_name":"Estimating WebRTC Video QoE Metrics Without Using Application Headers","publication_year":2023,"publication_date":"2023-10-23","ids":{"openalex":"https://openalex.org/W4379469712","doi":"https://doi.org/10.1145/3618257.3624828"},"language":"en","primary_location":{"id":"doi:10.1145/3618257.3624828","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3618257.3624828","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM on Internet Measurement Conference","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2306.01194","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071706660","display_name":"Taveesh Sharma","orcid":"https://orcid.org/0000-0002-2616-0390"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]},{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Taveesh Sharma","raw_affiliation_strings":["University of Chicago, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I40347166","https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063502119","display_name":"Tarun Mangla","orcid":"https://orcid.org/0000-0001-9016-9931"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Tarun Mangla","raw_affiliation_strings":["IIT Delhi, Delhi, India"],"affiliations":[{"raw_affiliation_string":"IIT Delhi, Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046100948","display_name":"Arpit Gupta","orcid":"https://orcid.org/0000-0002-6378-7440"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arpit Gupta","raw_affiliation_strings":["UCSB, Santa Barbara, CA, USA"],"affiliations":[{"raw_affiliation_string":"UCSB, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103258769","display_name":"Junchen Jiang","orcid":"https://orcid.org/0000-0002-6877-1683"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]},{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junchen Jiang","raw_affiliation_strings":["University of Chicago, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I40347166","https://openalex.org/I39422238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068586837","display_name":"Nick Feamster","orcid":"https://orcid.org/0000-0001-9315-5201"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]},{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nick Feamster","raw_affiliation_strings":["University of Chicago, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I40347166","https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5071706660"],"corresponding_institution_ids":["https://openalex.org/I39422238","https://openalex.org/I40347166"],"apc_list":null,"apc_paid":null,"fwci":3.2237,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.93619493,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"485","last_page":"500"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10138","display_name":"Network Traffic and Congestion Control","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8764336109161377},{"id":"https://openalex.org/keywords/webrtc","display_name":"WebRTC","score":0.7504043579101562},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.6140310764312744},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.5003635883331299},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.46711453795433044},{"id":"https://openalex.org/keywords/quality-of-experience","display_name":"Quality of experience","score":0.45455798506736755},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.4069567918777466},{"id":"https://openalex.org/keywords/quality-of-service","display_name":"Quality of service","score":0.20967569947242737},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.11842697858810425}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8764336109161377},{"id":"https://openalex.org/C2779790888","wikidata":"https://www.wikidata.org/wiki/Q1089715","display_name":"WebRTC","level":2,"score":0.7504043579101562},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.6140310764312744},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.5003635883331299},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.46711453795433044},{"id":"https://openalex.org/C2779333187","wikidata":"https://www.wikidata.org/wiki/Q3132648","display_name":"Quality of experience","level":3,"score":0.45455798506736755},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.4069567918777466},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.20967569947242737},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.11842697858810425},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C204787440","wikidata":"https://www.wikidata.org/wiki/Q188504","display_name":"Alternative medicine","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3618257.3624828","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3618257.3624828","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM on Internet Measurement Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2306.01194","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.01194","pdf_url":"https://arxiv.org/pdf/2306.01194","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2306.01194","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.01194","pdf_url":"https://arxiv.org/pdf/2306.01194","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1393793025","display_name":null,"funder_award_id":"2126327","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G207838915","display_name":null,"funder_award_id":"OAC-2126281","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2757131302","display_name":"IMR: MT: A Community Platform for Controlled Experiments on Internet Access Networks","funder_award_id":"2223610","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3620354760","display_name":null,"funder_award_id":"NS-2313190","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3736266085","display_name":"CSR: Medium: Improving the Interface between Machine Learning and Software Systems","funder_award_id":"2313190","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3777142569","display_name":null,"funder_award_id":"OAC-2126327","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4190001885","display_name":"CNS Core: Small: Closing the Reality Gap for Learning-Augmented Network Systems","funder_award_id":"2131826","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5064054989","display_name":"CAREER: Enabling Perception-Driven Optimization for Online Videos","funder_award_id":"2146496","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5953124651","display_name":"IMR: RI-P: Programmable Closed-loop Measurement Platform for Last-Mile Networks","funder_award_id":"2224687","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5993441864","display_name":null,"funder_award_id":"CNS-2124393,CNS-2223610,CNS-2224687,OAC-2126327,CNS-2146496,CNS-2131826,CNS-2313190,CNS-2003257,OAC-2126281","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G6755165505","display_name":null,"funder_award_id":"award","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6874958292","display_name":"Collaborative Research: CISE-ANR: CNS Core: Small: Modeling Modern Network Traffic: From Data Representation to Automated Machine Learning","funder_award_id":"2124393","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G856220545","display_name":"MLWiNS: RL-based Self-driving Wireless Network Management System for QoE Optimization","funder_award_id":"2003257","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8871307636","display_name":"CC* Integration-Large: Bringing Code to Data: A Collaborative  Approach to Democratizing Internet Data Science","funder_award_id":"2126281","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"},{"id":"https://openalex.org/F4320332500","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4379469712.pdf"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W95608104","https://openalex.org/W1550768496","https://openalex.org/W1654894100","https://openalex.org/W1991312813","https://openalex.org/W2000781198","https://openalex.org/W2026549660","https://openalex.org/W2099040451","https://openalex.org/W2152569540","https://openalex.org/W2155906977","https://openalex.org/W2164269242","https://openalex.org/W2466953229","https://openalex.org/W2512144135","https://openalex.org/W2555248751","https://openalex.org/W2622055490","https://openalex.org/W2734941459","https://openalex.org/W2790105919","https://openalex.org/W2790600716","https://openalex.org/W2798388120","https://openalex.org/W2885374715","https://openalex.org/W2894796097","https://openalex.org/W2898499449","https://openalex.org/W2909559157","https://openalex.org/W2972532057","https://openalex.org/W2972544683","https://openalex.org/W3011682783","https://openalex.org/W3095927306","https://openalex.org/W3099045918","https://openalex.org/W3109062337","https://openalex.org/W3124153473","https://openalex.org/W3194190187","https://openalex.org/W3201993040","https://openalex.org/W3210089935","https://openalex.org/W4229785328","https://openalex.org/W4239515884","https://openalex.org/W4244328360","https://openalex.org/W4307020320","https://openalex.org/W4307020449","https://openalex.org/W4318243594","https://openalex.org/W4324009859","https://openalex.org/W4379469712","https://openalex.org/W4382197758"],"related_works":["https://openalex.org/W134869038","https://openalex.org/W2790105919","https://openalex.org/W4254657552","https://openalex.org/W3123652108","https://openalex.org/W4298826893","https://openalex.org/W2467062953","https://openalex.org/W2218371916","https://openalex.org/W3111003943","https://openalex.org/W2992634480","https://openalex.org/W4364305686"],"abstract_inverted_index":{"The":[0],"increased":[1],"use":[2,46,74,96],"of":[3,18,49,97,114,137,180,183,233],"video":[4,124,155],"conferencing":[5],"applications":[6],"(VCAs)":[7],"has":[8],"made":[9],"it":[10],"critical":[11],"to":[12,38,65,82,110,153,211,231],"understand":[13],"and":[14,107,123,142,174,194],"support":[15],"end-user":[16],"quality":[17],"experience":[19],"(QoE)":[20],"by":[21,150],"all":[22,69],"stakeholders":[23],"in":[24,68,101,236],"the":[25,95,102,147,151,204,212,237,247],"VCA":[26,42,116,184],"ecosystem,":[27],"especially":[28],"network":[29,58,103,192],"operators,":[30],"who":[31],"typically":[32],"do":[33],"not":[34,61],"have":[35,63],"direct":[36],"access":[37,64],"client":[39],"software.":[40],"Existing":[41],"QoE":[43,117],"estimation":[44],"methods":[45],"passive":[47],"measurements":[48],"application-level":[50,248],"Real-time":[51],"Transport":[52],"Protocol":[53],"(RTP)":[54],"headers.":[55,250],"However,":[56],"a":[57,128,135],"operator":[59],"does":[60],"always":[62],"RTP":[66,76,249],"headers":[67],"cases,":[70],"particularly":[71],"when":[72],"VCAs":[73,152,166],"custom":[75],"protocols":[77],"(e.g.,":[78,85,140],"Zoom)":[79],"or":[80],"due":[81],"system":[83],"constraints":[84],"legacy":[86],"measurement":[87],"systems).":[88],"Given":[89],"this":[90,92],"challenge,":[91],"paper":[93],"considers":[94],"more":[98],"standard":[99],"features":[100,143],"traffic,":[104],"namely,":[105],"IP":[106],"UDP":[108],"headers,":[109],"provide":[111],"per-second":[112],"estimates":[113],"key":[115],"metrics":[118],"such":[119],"as":[120],"frames":[121,156],"rate":[122],"resolution.":[125],"We":[126,159,201],"develop":[127],"method":[129,162],"that":[130,203],"uses":[131],"machine":[132],"learning":[133],"with":[134],"combination":[136],"flow":[138],"statistics":[139],"throughput)":[141],"derived":[144],"based":[145],"on":[146],"mechanisms":[148],"used":[149],"fragment":[154],"into":[157],"packets.":[158],"evaluate":[160],"our":[161],"for":[163,229],"three":[164],"prevalent":[165],"running":[167],"over":[168],"WebRTC:":[169],"Google":[170],"Meet,":[171],"Microsoft":[172],"Teams,":[173],"Cisco":[175],"Webex.":[176],"Our":[177],"evaluation":[178],"consists":[179],"54,696":[181],"seconds":[182],"data":[185],"collected":[186],"from":[187,198],"both":[188],"(1),":[189],"controlled":[190],"in-lab":[191],"conditions,":[193],"(2)":[195],"real-world":[196,238],"networks":[197],"15":[199],"households.":[200],"show":[202],"ML-based":[205],"approach":[206],"yields":[207],"similar":[208],"accuracy":[209],"compared":[210],"RTP-based":[213],"methods,":[214],"despite":[215],"using":[216,246],"only":[217,242],"IP/UDP":[218],"data.":[219],"For":[220],"instance,":[221],"we":[222],"can":[223],"estimate":[224],"FPS":[225,228],"within":[226],"2":[227],"up":[230],"83.05%":[232],"one-second":[234],"intervals":[235],"data,":[239],"which":[240],"is":[241],"1.76%":[243],"lower":[244],"than":[245]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}