{"id":"https://openalex.org/W4200095346","doi":"https://doi.org/10.1109/vtc2021-fall52928.2021.9625496","title":"A Deep Reinforcement Learning Approach for Point Cloud Video Transmissions","display_name":"A Deep Reinforcement Learning Approach for Point Cloud Video Transmissions","publication_year":2021,"publication_date":"2021-09-01","ids":{"openalex":"https://openalex.org/W4200095346","doi":"https://doi.org/10.1109/vtc2021-fall52928.2021.9625496"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2021-fall52928.2021.9625496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2021-fall52928.2021.9625496","pdf_url":null,"source":{"id":"https://openalex.org/S4363607774","display_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043997801","display_name":"Hai Lin","orcid":"https://orcid.org/0000-0001-6708-2475"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai Lin","raw_affiliation_strings":["Zhengzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052006689","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0002-2380-8681"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Zhengzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072419341","display_name":"Yangjie Cao","orcid":"https://orcid.org/0000-0002-1170-4340"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangjie Cao","raw_affiliation_strings":["Zhengzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100603421","display_name":"Zhi Liu","orcid":"https://orcid.org/0000-0003-0537-4522"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhi Liu","raw_affiliation_strings":["University of Electro-Communications, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electro-Communications, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065252700","display_name":"Xianfu Chen","orcid":"https://orcid.org/0000-0002-9453-4200"},"institutions":[{"id":"https://openalex.org/I87653560","display_name":"VTT Technical Research Centre of Finland","ror":"https://ror.org/04b181w54","country_code":"FI","type":"nonprofit","lineage":["https://openalex.org/I4210089493","https://openalex.org/I87653560"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Xianfu Chen","raw_affiliation_strings":["VTT Technical Research Centre of Finland, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VTT Technical Research Centre of Finland, Finland","institution_ids":["https://openalex.org/I87653560"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11569","display_name":"Optical Coherence Tomography Applications","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9686999917030334,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7873144745826721},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7764710187911987},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.650139331817627},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.52635657787323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48506608605384827},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.12399643659591675}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7873144745826721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7764710187911987},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.650139331817627},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.52635657787323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48506608605384827},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.12399643659591675}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2021-fall52928.2021.9625496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2021-fall52928.2021.9625496","pdf_url":null,"source":{"id":"https://openalex.org/S4363607774","display_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6100000143051147}],"awards":[{"id":"https://openalex.org/G8490082499","display_name":"\u57fa\u4e8e\u7fa4\u667a\u878d\u5408\u8ba1\u7b97\u7684\u4eba\u673a\u534f\u540c\u611f\u77e5\u6280\u672f\u7814\u7a76","funder_award_id":"61972092","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2039725334","https://openalex.org/W2778629106","https://openalex.org/W2799179991","https://openalex.org/W2906364736","https://openalex.org/W2913109850","https://openalex.org/W2939432951","https://openalex.org/W2943596644","https://openalex.org/W2964233199","https://openalex.org/W3010949162","https://openalex.org/W3016063666","https://openalex.org/W3040643035","https://openalex.org/W3048491269","https://openalex.org/W3085699395","https://openalex.org/W3100379301","https://openalex.org/W3119142142","https://openalex.org/W3163279171","https://openalex.org/W3211091494","https://openalex.org/W6782129533"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4244478748","https://openalex.org/W3150465815","https://openalex.org/W4223488648","https://openalex.org/W2134969820","https://openalex.org/W2251605416","https://openalex.org/W1997222214","https://openalex.org/W2560439919"],"abstract_inverted_index":{"The":[0],"point":[1,28],"cloud":[2,29],"videos,":[3],"thanks":[4],"to":[5,22,69,86,113,119],"the":[6,23,33,43,52,71,76,79,82,88,91,115,120,127,133,143],"multi-view":[7],"and":[8,19,48],"immersive":[9],"experiences,":[10],"have":[11],"recently":[12],"attracted":[13],"notable":[14],"attentions":[15],"from":[16],"both":[17],"academia":[18],"industry.":[20],"Due":[21],"high":[24],"data":[25,109],"volume,":[26],"a":[27,63],"video":[30,93,128],"also":[31,131],"raises":[32],"challenge":[34],"of":[35,42,90,139],"quality-of-experience":[36],"(QoE),":[37],"which":[38],"is":[39],"in":[40,96],"terms":[41],"balance":[44],"between":[45],"playback":[46],"quality":[47,129],"buffering":[49],"delay":[50],"during":[51],"transmission":[53],"under":[54],"time-varying":[55],"system":[56],"conditions.":[57],"In":[58],"this":[59],"paper,":[60],"we":[61],"propose":[62],"deep":[64],"reinforcement":[65],"learning":[66],"(DRL)":[67],"approach":[68,84,123],"optimize":[70],"expected":[72],"long-term":[73],"QoE":[74,141],"for":[75,94,142],"client.":[77],"Over":[78],"time":[80],"horizon,":[81],"proposed":[83,116],"learns":[85],"select":[87],"tiles":[89],"corresponding":[92],"transmissions":[95],"an":[97,137],"iterative":[98],"way.":[99],"Under":[100],"various":[101],"settings,":[102],"numerical":[103],"experiments":[104],"based":[105],"on":[106],"real":[107],"throughput":[108],"traces":[110],"are":[111],"conducted":[112],"evaluate":[114],"approach.":[117],"Compared":[118],"baselines,":[121],"our":[122],"not":[124],"only":[125],"enhances":[126],"but":[130],"reduces":[132],"re-buffering":[134],"time,":[135],"obtaining":[136],"improvement":[138],"average":[140],"client":[144],"by":[145],"9%&#x2013;14%.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
