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Look ahead to improve QoE in DASH streaming

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Look ahead to improve QoE in DASH streaming

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dc.contributor.author Belda Ortega, Román es_ES
dc.contributor.author De Fez Lava, Ismael es_ES
dc.contributor.author Arce Vila, Pau es_ES
dc.contributor.author Guerri Cebollada, Juan Carlos es_ES
dc.date.accessioned 2021-05-28T03:32:39Z
dc.date.available 2021-05-28T03:32:39Z
dc.date.issued 2020-09 es_ES
dc.identifier.issn 1380-7501 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166899
dc.description.abstract [EN] When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature. es_ES
dc.description.sponsorship This work is supported by the PAID-10-18 Program of the Universitat Politecnica de Valencia (Ayudas para contratos de acceso al sistema espanol de Ciencia, Tecnologia e Innovacion, en estructuras de investigacion de la Universitat Politecnica de Valencia) and by the Project 20180810 from the Universitat Politecnica de Valencia ("Tecnologias de distribucion y procesado de informacion multimedia y QoE"). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Multimedia Tools and Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Adaptive bitrate streaming (ABR) es_ES
dc.subject Dynamic adaptive streaming over HTTP (DASH) es_ES
dc.subject Quality of Experience (QoE) es_ES
dc.subject Video Multimethod Assessment Fusion (VMAF) es_ES
dc.subject ExoPlayer es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Look ahead to improve QoE in DASH streaming es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11042-020-09214-9 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-10-18/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//20180810/ es_ES
dc.rights.accessRights Abierto 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.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Belda Ortega, R.; De Fez Lava, I.; Arce Vila, P.; Guerri Cebollada, JC. (2020). Look ahead to improve QoE in DASH streaming. Multimedia Tools and Applications. 79(33-34):25143-25170. https://doi.org/10.1007/s11042-020-09214-9 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11042-020-09214-9 es_ES
dc.description.upvformatpinicio 25143 es_ES
dc.description.upvformatpfin 25170 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 79 es_ES
dc.description.issue 33-34 es_ES
dc.relation.pasarela S\417344 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
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