Investigation into User-Centric QoE and Network-Centric Parameters for YouTube Service on Mobile Networks

Anchuen, Patikorn; Uthansakul, Peerapong

doi:10.1145/3330180.3330186

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The presented results are limited in scope as their setup streams a custom video hosted on their own server; by omitting evaluation of a popular streaming service, such as YouTube or Netflix, the work does not accurately capture the application and network performance experienced by actual users. [9] undertakes a study similar to ours, however, with a modest dataset that is limited to a small portion of a local transit route and is thus difficult to generalize.…”

Section: Related Workmentioning

confidence: 99%

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Adarsh

Nekrasov

Paul

et al. 2021

Passive and Active Measurement

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The explosion of mobile broadband as an essential means of Internet connectivity has made the scalable evaluation and inference of quality of experience (QoE) for applications delivered over LTE networks critical. However, direct QoE measurement can be time and resource intensive. Further, the wireless nature of LTE networks necessitates that QoE be evaluated in multiple locations per base station as factors such as signal availability may have significant spatial variation. Based on our observations that quality of service (QoS) metrics are less time and resource-intensive to collect, we investigate how QoS can be used to infer QoE in LTE networks. Using an extensive, novel dataset representing a variety of network conditions, we design several state-of-the-art predictive models for scalable video QoE inference. We demonstrate that our models can accurately predict rebuffering events and resolution switching more than 80% of the time, despite the dataset exhibiting vastly different QoS and QoE profiles for the location types. We also illustrate that our classifiers have a high degree of generalizability across multiple videos from a vast array of genres. Finally, we highlight the importance of low-cost QoS measurements such as reference signal received power (RSRP) and throughput in QoE inference through an ablation study.

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Section: Related Workmentioning

confidence: 99%

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Adarsh

Nekrasov

Paul

et al. 2021

Passive and Active Measurement

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show abstract

“…Therefore, the method should be flexible and effective for comprehensive implementation. An objective function for implementing the real environment should be created using datasets compiled from subjective testing [21]. Generally, models are often created from datasets consisting of QoS and OS.…”

Section: Introductionmentioning

confidence: 99%

QoE Analysis for Improving Multimedia Services Based on Different Operating Situations on Cellular Networks

Anchuen

Uthansakul

2021

IEEE Access

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Cellular networks are constantly evolving to support the new context and demands of users with increasing internet traffic. The key to a service provider's success is a direct response to endpoints to retain existing users and attract new users. For this reason, Mobile Network Operators (MNOs) must reach user satisfaction under a user-centered perspective with Quality of Experience (QoE) to achieve a Return on Investment (RoI). In the latest QoE modelling, our previous method performed QoE estimation and synthesis using an Artificial Neural Network (ANN). Nevertheless, such work did not prove the characteristics of the QoE model built on different working situations. The essential features of the area and user diversities need to be considered in detail to summarize the proper model. In this paper, the proposed framework focuses on analyzing QoE model characteristics under different operating situations for comprehensive implementation. The experimental results reveal that the created QoE models from areas of different densities affect the different predictive variables. Inter-Rater Reliability (IRR) analysis exposes the reliability of datasets gathered from users of different groups to distinguish the effects on the response of significant parameters. Additionally, the summary specifies the characteristics of the appropriate model by area and user group as a guideline for improving the multimedia services.

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QoS to QoE Mapping Based on Optimised BP Neural Networks

Wu,

Li,

Zhang

et al. 2023

2023 8th International Conference on Image, Vision and Computing (ICIVC)

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Investigation into User-Centric QoE and Network-Centric Parameters for YouTube Service on Mobile Networks

Cited by 4 publications

References 8 publications

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

QoE Analysis for Improving Multimedia Services Based on Different Operating Situations on Cellular Networks

QoS to QoE Mapping Based on Optimised BP Neural Networks

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