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New objective QoE models for evaluating ABR algorithms in DASH

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New objective QoE models for evaluating ABR algorithms in DASH

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dc.contributor.author De Fez Lava, Ismael es_ES
dc.contributor.author Belda Ortega, Román es_ES
dc.contributor.author Guerri Cebollada, Juan Carlos es_ES
dc.date.accessioned 2021-05-13T03:32:25Z
dc.date.available 2021-05-13T03:32:25Z
dc.date.issued 2020-05-15 es_ES
dc.identifier.issn 0140-3664 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166269
dc.description.abstract [EN] As users become more demanding with regards to the consumption of multimedia content, the importance of measuring their level of satisfaction is growing. The difficulty in terms of time and resources for assessing the Quality of Experience (QoE) has popularized the use of objective QoE models, which try to emulate human behavior regarding the playback of multimedia streaming. Some objective QoE models existing in the literature are based on the bitrate. However, the PSNR (Peak Signal-to-Noise Ratio) or VMAF (Video Multimethod Assessment Fusion) have been proved to be metrics with a closer relationship with the QoE than the bitrate. This paper proposes three new models to measure the QoE analytically in DASH (Dynamic Adaptive Streaming over HTTP) video services. The first is based on the bitrate of the displayed video segments, whereas the second and the third are based on the PSNR and VMAF of each video segment, respectively. The proposed models are compared to the ITU-T standard P.1203 as well as the bitrate-based QoE model proposed by Yin et al. Moreover, the paper presents a subjective study, which confirms the validity of the proposed models. The models are validated by using different DASH adaptation algorithms. In this sense, this paper also presents a DASH ABR (Adaptive Bitrate Streaming) algorithm called Look Ahead, which takes into account the inherent bitrate variability of the video encoding process in order to calculate, in real time, the appropriate quality level that minimizes the number of stalls during the playback. es_ES
dc.description.sponsorship This work is supported by the PAID-10-18 Program and by the R&D Line "Technologies for distribution and processing of multimedia information and QoE'' from the Universitat Politecnica de Valencia. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computer Communications es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Quality of Experience (QoE) es_ES
dc.subject Dynamic Adaptive Streaming over HTTP (DASH) es_ES
dc.subject Peak Signal-to-Noise Ratio (PSNR) es_ES
dc.subject Video Multimethod Assessment Fusion (VMAF) es_ES
dc.subject Adaptive Bitrate Streaming (ABR) es_ES
dc.subject ITU-T P.1203 es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title New objective QoE models for evaluating ABR algorithms in DASH es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.comcom.2020.05.011 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-10-18/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.description.bibliographicCitation De Fez Lava, I.; Belda Ortega, R.; Guerri Cebollada, JC. (2020). New objective QoE models for evaluating ABR algorithms in DASH. Computer Communications. 158:126-140. https://doi.org/10.1016/j.comcom.2020.05.011 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.comcom.2020.05.011 es_ES
dc.description.upvformatpinicio 126 es_ES
dc.description.upvformatpfin 140 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 158 es_ES
dc.relation.pasarela S\415166 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
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