A roadmap on QoE metrics and models

Liotou, Eirini; Tsolkas, Dimitris; Passas, Nikos

doi:10.1109/ict.2016.7500363

Cited by 25 publications

(13 citation statements)

References 17 publications

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“…The model then maps network parameters (e.g. network delay, jitter, packet loss rate and/or other) to MOS values [25]. However, the development of such models for QoE evaluation for ODV streaming is still expected.…”

Section: Related Workmentioning

confidence: 99%

Subjective Evaluation of User Quality of Experience for Omnidirectional Video Streaming

Mrvelj

Matulin

Martirosov

2020

PROMET

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This paper reports on the results of subjective testing of user Quality of Experience (QoE) for omnidirectional video (ODV) streaming quality. The test was conducted among 20 test subjects who watched three ODVs using a Head Mounted Display (HMD) system. The length of the videos was between two and three minutes. The first video was used for training purposes and contained no quality degradations. The quality of the other two ODVs was degraded by manipulating the resolution or by introducing different frame drop patterns. While watching the pre-prepared videos the subjects indicated if they noticed the changes in the quality and then rated it. After watching each video, the subjects completed a separate questionnaire, which evaluated their level of enjoyment and discomfort with the video. The results showed that the degradation of both objective parameters (video resolution and frame rate) impacted the subjects’ perception of quality; however, the impact was somewhat alleviated in ODV which contained dynamic scenes and fast camera movements.

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Section: Related Workmentioning

confidence: 99%

Subjective Evaluation of User Quality of Experience for Omnidirectional Video Streaming

Mrvelj

Matulin

Martirosov

2020

PROMET

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“…However, in [9] the whole evaluation process has been presented, and it does not include the influence of different user factors for QoE. The forecast model in [9] was optimized in the development [12,13], considering user viewing time, pause time, and other factors [14,15]. However, there is no concrete formula, it was just proved that taking into account user behavior factors can increase the QoE forecasting model accuracy.…”

Section: Related Workmentioning

confidence: 99%

Method for Monitoring and Increasing the Quality of Experience in Cellular Networks Based on Interconnections Between Key Quality Indicators Using Spline-Approximation

Al-Azzeh

Одарченко²,

Abakumova

et al. 2020

Preprint

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Sustainable and balanced development of the communications industry is inextricably linked with increasing numbers of subscribers and services provided. The current level of development of the cellular services market, qualitative changes in the means and methods of providing services, and the increased volume and diversity of information circulating in cellular networks have evoked the need for service quality assessment system improvement. To maintain competitiveness, the main efforts of operators are aimed at improving quality and increasing the service life of subscribers in the network by ensuring the required level of customer satisfaction in high quality services through a system of organizational-technical and socio-economic measures to bring the achieved level of quality of service (QoS) provision in accordance with the existing, emerging, or projected needs of subscribers. These circumstances determine the relevance of the studied tasks related to the assessment and management of the quality of cellular services and the development of approaches to their solution.To achieve improved QoS provision, a functional relationship between network parameters has been established, i.e., the impact of key performance indicators on a key quality indicator through the use of cubic Hermitian splines (CHS) has been determined. The use of splines, as a signal model, can significantly improve the quality of signal processing due to the continuity of values and partial derivatives in the joints of spline gluing. CHS are characterized by calculation simplicity, as they provide high speed computing, which, in turn, is important for real time work when processing large data sets. Experimentally, it was shown that the use of splines allows for the ability to calculate statistical estimates of the required parameters of spline approximations, but also their confidence intervals, which increases the accuracy and probability of further calculations and is a distinct advantage of this approachThe methods of service quality management through machine learning have been improved, which are effective tools for modeling integrated indicators of communication services quality control, monitoring their condition in terms of final copies of services, determining the causes of degradation, and reporting. It monitors the service performance provided by the cellular operator. In addition, the method checks the readiness and availability of services, detects network nodes through which quality degradation occurs, collects various quality metrics, and compares them with pre-installed quality indicators.

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“…These methods are based on machine learning methods, and they use a subjective dataset as the input for the learning process. The advantage of these methods is efficiency and real‐time execution …”

Section: Proposalmentioning

confidence: 99%

Q2ABR: QoE‐aware adaptive video bit rate solution

Amour

Souihi

Mellouk

2019

Int J Communication

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KEYWORDSadaptive bit rate streaming (ABR), breakpoint detection (BPD), boosting gradient regression (GBR), controlled-laboratory, mean opinion score (MOS), machine learning (ML), quality of experience (QoE), reinforcement learning, QoE assessment, streaming video Abbreviations: ABR, adaptive bit rate streaming; BPD, breakpoint detection; GBR, boosting gradient regression; ML, machine learning; MOS, mean opinion score; QoE, quality of experience; RL, reinforcement learning.Int J Commun Syst. 2020;33:e4204.wileyonlinelibrary.com/journal/dac

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A roadmap on QoE metrics and models

Cited by 25 publications

References 17 publications

Subjective Evaluation of User Quality of Experience for Omnidirectional Video Streaming

Subjective Evaluation of User Quality of Experience for Omnidirectional Video Streaming

Method for Monitoring and Increasing the Quality of Experience in Cellular Networks Based on Interconnections Between Key Quality Indicators Using Spline-Approximation

Q2ABR: QoE‐aware adaptive video bit rate solution

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