Bounding the maximum sampling rate when measuring PLP in a packet buffer

Wahid, Amna Abdul; Schormans, J.A.

doi:10.1109/ceec.2013.6659456

Cited by 3 publications

(2 citation statements)

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“…Roughan [23] and [24] has also reported bounds on the accuracy and precision of using probes for measuring packet loss, and has tried to quantify the error in packet loss using statistical techniques. The size of the probes, sampling frequency of the probes and the load on the network can also cause inaccurate measurements as presented in [7][8][9][10], [20], [22] and [25].…”

Section: Related Workmentioning

confidence: 99%

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Statistical Error Propagation Aﬀecting the Quality of Experience Evaluation in Video on Demand Applications

Wahab¹,

Schormans²,

Ahmad³

2019

Preprint

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In addition to the traditional QoS metrics of delay, delay jitter, and packet loss probability (PLP), Quality of Experience (QoE) is now widely accepted as a numerical proxy for actual user experience. The literature has reported many mathematical mappings between QoE and QoS. These QoS parameters are measured by the network providers using sampling. There are some papers studying sampling errors in QoS measurements; however there is no account of propagation of these sampling errors to QoE evaluation. In this paper, we used industrially acquired measurements of PLP and jitter to evaluate the sampling errors and correlation in measurements. Focussing on Video-on-demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLP and jitter. The resulting mathematical functions of QoE and theory of error propagation was used to evaluate the propagated error in QoE, and this error was represented as confidence interval. Using the guidelines of UK government for sampling, our results indicate that confidence intervals around estimated QoE in a busy hour can be between MOS=1 to MOS=5 at targeted operating point of QoS parameters. These results are a new perspective on QoE evaluation, and are of great significance to all organisations that need to estimate the QoE VoD applications precisely.

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Section: Related Workmentioning

confidence: 99%

“…[20]. Since QoE can be represented as a mathematical expression in terms of QoS parameters using the uncertainty propagation techniques presented in [25], the uncertainty in QoE is investigated. The evaluation of the propagated uncertainty is commonly used in different fields of biochemistry [26], physics [27] and psychology [28].…”

Section: Uncertainty Analysismentioning

confidence: 99%

Statistical Error Propagation Aﬀecting the Quality of Experience Evaluation in Video on Demand Applications

Wahab¹,

Schormans²,

Ahmad³

2019

Preprint

Self Cite

View full text Add to dashboard Cite

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Bounds on accuracy when estimating the loss probability in a packet buffer

Wahid

Schormans

2015

2015 7th Computer Science and Electronic Engineering Conference (CEEC)

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Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

2020

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In addition to the traditional Quality of Service (QoS) metrics of latency, jitter and Packet Loss Ratio (PLR), Quality of Experience (QoE) is now widely accepted as a numerical proxy for the actual user experience. The literature has reported many mathematical mappings between QoE and QoS, where the QoS parameters are measured by the network providers using sampling. Previous research has focussed on sampling errors in QoS measurements. However, the propagation of these sampling errors in QoS through to the QoE values has not been evaluated before. This is important: without knowing how sampling errors propagate through to QoE estimates there is no understanding of the precision of the estimates of QoE, only of the average QoE value. In this paper, we used industrially acquired measurements of PLR and jitter to evaluate the sampling errors. Additionally, we evaluated the correlation between these QoS measurements, as this correlation affects errors propagating to the estimated QoE. Focusing on Video-on-Demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLR and jitter. The resulting mathematical functions, and the theory of error propagation, were used to evaluate the error propagated to QoE. This error in estimated QoE was represented as confidence interval width. Using the guidelines of UK government for sampling in a busy hour, our results indicate that confidence intervals around estimated the Mean Opinion Score (MOS) rating of QoE can be between MOS = 1 to MOS = 4 at targeted operating points of the QoS parameters. These results are a new perspective on QoE evaluation and are of potentially great significance to all organisations that need to estimate the QoE of VoD applications precisely.

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Bounding the maximum sampling rate when measuring PLP in a packet buffer

Cited by 3 publications

References 15 publications

Statistical Error Propagation Aﬀecting the Quality of Experience Evaluation in Video on Demand Applications

Statistical Error Propagation Aﬀecting the Quality of Experience Evaluation in Video on Demand Applications

Bounds on accuracy when estimating the loss probability in a packet buffer

Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

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