Video quality assessment in video streaming services considering user preference for video content

Rodríguez, Demóstenes Zegarra; Rosa, Renata Lopes; Costa, Eduardo; Abrahão, Júlia Issy; Bressan, Graça

doi:10.1109/tce.2014.6937328

Cited by 38 publications

(9 citation statements)

References 34 publications

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“…In QoE studies, several models have been proposed in both wired and wireless networks, some of them were presented and evaluated in [54], [55], [56] and [57]. These models are not well adapted to VANETs since they do not take into account their specificities.…”

Section: Qoe Models For Video Streaming Over Vanetmentioning

confidence: 99%

Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

2019

CIT. J. comput. inf. technol

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Vehicular Ad-hoc Networks (VANETs) are a new emerging technology which has attracted enormous interest over the last few years. It enables vehicles to communicate with each other and with roadside infrastructures for many applications. One of the promising applications is multimedia services for traffic safety or infotainment. The video service requires a good quality to satisfy the end-user known as the Quality of Experience (QoE). Several models have been suggested in the literature to measure or predict this metric. In this paper, we present an overview of interesting researches, which propose QoE models for video streaming over VANETs. The limits and deficiencies of these models are identified, which shed light on the challenges and real problems to overcome in the future.

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Section: Qoe Models For Video Streaming Over Vanetmentioning

confidence: 99%

Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

2019

CIT. J. comput. inf. technol

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“…In [10] the researcher looks at video streaming services taking into consideration user preference for video content. This paper examines the different types of video content and evaluates if a different level of quality is needed for different content.…”

Section: Related Workmentioning

confidence: 99%

(DPMLA)-weighted dynamic characteristics and predictable movement learning algorithm to improve video streaming in heterogeneous environments

Maher

Banks

Fallon

2015

2015 IEEE International Symposium on Systems Engineering (ISSE)

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Mobile video is a key driver in the growth of mobile data. In heterogeneous networking environments, multimedia sessions are particularly vulnerable to varying network capabilities of underlying networks. This paper proposes a weighted dynamic and predictable based learning algorithm to improve video streaming in heterogeneous network environments (DPMLA). Current handover methods for seamless video streaming are performance limited as they do not consider how predictability movement can be used to alter the network handover decision. Research has shown that 93% of human movement is predictable. Studies also suggest that end user movement can be reliably predicted using mobile telecom services. The DPMLA algorithm considers both the dynamic performance of the network (Received Signal Strength (RSS), delay, loss) with a measure of the predictability of end user movement. Results illustrate that the DPMLA algorithm optimizes network selection and improves overall video streaming performance.

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“…This kind of services, like IPTV and VOD, became very popular in the last years, generating an enormous amount of data, with video streaming being the most popular one [5], running in devices such as smartphones, desktops, wire-less computer, and tablets [6]. With the increase of data being transmitted, the actual systems have to be upgraded to guarantee the quality of the service.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Impairment Caused by MPEG Video Frame Loss

Silva¹,

Miguel²,

Sauer³

et al. 2017

ENG

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This article presents a study on the impact of video frame losses on the quality perceived by users. Video compression standards, such as MPEG, use a sequence of frames called Group of Pictures (GOP), which is a video compression method which a frame is expressed in terms of one or more neighboring frames. This dependence between frames impacts directly in the quality because a loss of a reference frame prevents the decoding of other frames in GOP, thereby reducing the user-perceived quality. The assessment of quality in this article is estimated by Peak Signal Noise Ratio (PSNR), which compares the original and the received images. Computer simulations were used to show that the degradation on the quality may vary for different patterns of GOPs and type of lost frames.

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Video quality assessment in video streaming services considering user preference for video content

Cited by 38 publications

References 34 publications

Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

(DPMLA)-weighted dynamic characteristics and predictable movement learning algorithm to improve video streaming in heterogeneous environments

Evaluation of Impairment Caused by MPEG Video Frame Loss

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