Modelo para la medición de QoE en IPTV

Quiñones, Juan Carlos Cuéllar; Arciniegas, H. Jose L.; Ortíz, Jesús Hamilton; Madrid, Universiad Autonoma de

doi:10.18046/eui/ee.3.2018

Cited by 2 publications

(3 citation statements)

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“…The models are represented by Equation (1), where X i and the coefficients represent the factors (QoS parameters: Delay, Jitter and Packet loss), β i represents the regression coefficients, and Y represents the response of interest (QoE) and ε is the random error. The entire detailed mathematical process is presented in [7]:…”

Section: Mathematical Formulation Of Our Proposed Modelsmentioning

confidence: 99%

“…The least squares estimator vector, β, is obtained by minimizing Equation (7), now expressed in matrix form:…”

Section: Mathematical Formulation Of Our Proposed Modelsmentioning

confidence: 99%

“…Subjective methods employ user surveys, whereby individuals evaluate a service in a real environment, answering a questionnaire that reflects the characteristics of the service, providing the supplier with a quantitative indicator of QoE for the evaluated service. Users evaluate several aspects of the service using a discrete scale (5-Excellent, 4-Good, 3-Fair, 2-Poor and 1-Bad), and the mean opinion score (MOS) is used as the metric for each aspect [7]. ITU-R and ITU-T have several recommendations for performing subjective video assessments, including methodologies for subjective assessment tests, criteria for observer selection, assessment procedures and data analysis methods.…”

Section: Introductionmentioning

confidence: 99%

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QoS/QoE in Flying Ad Hoc Networks Applied in Natural Disasters

Monedero¹,

Herrera

Quiñones

et al. 2022

Applied Sciences

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In this work, a group of mechanisms are exposed to provide quality of experience in flying ad hoc networks using a swarm of drones in a natural disaster service. End-to-end video traffic was analyzed. The metrics used to experimentally measure QoE/QoS are: delay, jitter and packet loss. The experience quality was evaluated before the disaster (C00), at the moment (B00) and after the disaster (I00). The methodology used to perform the design was experimental, and the NS simulator was used to evaluate the behavior of the swarm of drones connected through a flying ad hoc network. To perform data analysis, treatment and repetitions related to video traffic, the response surface methodology (MSR) was used, which is a set of mathematical techniques in order to optimize the obtained responses. The composite core design (DCC) was also used as it was the best fit to our experiment due to its flexibility. Since the quality of the experience was evaluated at three moments, the quality of services was also analyzed with three metrics. The main contributions of the research are a mathematical model of the quality of the experience based on the quality of the service; an experiment design using the end-to-end NS simulator; a methodology for the mathematical and statistical analysis of the data obtained; an algorithm that allows, from service quality metrics, to obtain the quality of the experience for end-to-end video traffic; and a proposal for future work for data analysis in a physical environment and applied to the environmental sector.

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Section: Mathematical Formulation Of Our Proposed Modelsmentioning

confidence: 99%

“…The least squares estimator vector, β, is obtained by minimizing Equation (7), now expressed in matrix form:…”

Section: Mathematical Formulation Of Our Proposed Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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