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A multi-layer probing approach for video over 5G in vehicular scenarios

2022-12Journal article Open access
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dc.contributor.authorLOPES, RUI
dc.contributor.authorRocha, Filipe
dc.contributor.authorSargento, Susana
dc.contributor.authorLuís, Miguel
dc.contributor.authorLeitão, Ricardo
dc.contributor.authorMarques, Eduardo
dc.contributor.authorAntunes, Bruno
dc.date.accessioned2023-02-10T14:13:52Z
dc.date.available2023-02-10T14:13:52Z
dc.date.issued2022-12
dc.description.abstractFifth generation (5G) technologies are becoming a reality throughout the world. In parallel, vehicular networks rise their pace in terms of utilization; moreover, multimedia content transmissions are also getting an always increasing demand by their users. Besides the promised performance of 5G networks, several questions still arise among the community: are these networks capable of delivering high quality video streaming services in moving scenarios? What is the relationship between the network conditions and the video quality of experience? To answer to the previous questions, in this paper we propose a multi-layer probing approach able to assess video transmissions over 5G and 4G, combining data from all layers of a communication model, relating events from its origin layers. The probe's potential is thoroughly evaluated in two distinct video streaming use cases, both targeting a vehicular scenario supported by cellular 4G and 5G networks. Regarding the probe's performance, we show that a multitude of performance and quality indicators, from different stack layers, can be obtained. As for the performance of 4G and 5G networks in video streaming scenarios, the results have shown that the 5G links show a better overall performance in terms of video quality-of-experience, granting lower delays and jitter conditions, thus allowing video delay to be diminished and segment buffering to be better performed in comparison to 4G, while still showing adaptability in lightly traffic-saturated vehicular-to-vehicular scenarios.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationLOPES, Rui; [et al] – A multi-layer probing approach for video over 5G in vehicular scenarios. Vehicular Communications. ISSN 2214-2096. Vol. 38 (2022), pp. 1-15.pt_PT
dc.identifier.doi10.1016/j.vehcom.2022.100534pt_PT
dc.identifier.issn2214-2096
dc.identifier.urihttp://hdl.handle.net/10400.21/15543
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationPOCI-01-0247-FEDER-038190pt_PT
dc.relationPOCI-01-0247-FEDER-112314pt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S221420962200081Xpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectVideo transmissionpt_PT
dc.subject5Gpt_PT
dc.subjectMPEG-DASHpt_PT
dc.subjectKey performance indicatorspt_PT
dc.subjectMobile networkspt_PT
dc.titleA multi-layer probing approach for video over 5G in vehicular scenariospt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage15pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleVehicular Communicationspt_PT
oaire.citation.volume38pt_PT
person.familyNameDE FIGUEIREDO ARNAY LOPES
person.familyNameSargento
person.familyNameLuís
person.familyNameAntunes
person.givenNameRUI EDUARDO
person.givenNameSusana
person.givenNameMiguel
person.givenNameBruno
person.identifier.ciencia-id981D-8333-2DB1
person.identifier.ciencia-id6C11-2B5E-FACB
person.identifier.ciencia-id3418-A2F5-3CA4
person.identifier.ciencia-idA311-7497-05F4
person.identifier.orcid0000-0002-3222-5197
person.identifier.orcid0000-0001-8761-8281
person.identifier.orcid0000-0003-3488-2462
person.identifier.orcid0000-0002-6969-6806
person.identifier.scopus-author-id6603312796
person.identifier.scopus-author-id36164286400
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication53103c47-98f8-41c4-8e38-146d6831c7ca
relation.isAuthorOfPublicatione0814076-e21c-4aab-beeb-0a06d3b01e24
relation.isAuthorOfPublication2eadcc1c-ff03-403a-9c42-f2e45f0fd528
relation.isAuthorOfPublication31c39faf-fc55-4234-80a1-2099d474d266
relation.isAuthorOfPublication.latestForDiscovery2eadcc1c-ff03-403a-9c42-f2e45f0fd528

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