QoE estimation and prediction using hidden Markov models in heterogeneous access networks

Mitra, Karan; Åhlund, Christer; Zaslavsky, Arkady

doi:10.1109/atnac.2012.6398083

Cited by 9 publications

(20 citation statements)

References 12 publications

(26 reference statements)

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“…A presented prototype implementation is tested in scenarios representing real VoIP service usage. We note that VoIP QoE estimation and prediction based on passive probing mechanisms and integrated directly into a mobility management protocol is further addressed by Mitra et al [93]. Furthermore, a user-centric approach to proposing seamless mobility management solutions was one of the key focus areas of the EU FP7 PERIMITER project [94] (available deliverables provide detailed insight into usercentric mobility design).…”

Section: A Survey Of Qoe Monitoring and Measurement Approachesmentioning

confidence: 99%

Survey and Challenges of QoE Management Issues in Wireless Networks

Baraković

Skorin‐Kapov

2013

Journal of Computer Networks and Communications

120

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With the move towards converged all-IP wireless network environments, managing end-user Quality of Experience (QoE) poses a challenging task, aimed at meeting high user expectations and requirements regarding reliable and cost-effective communication, access to any service, anytime and anywhere, and across multiple operator domains. In this paper, we give a survey of state-of-the-art research activities addressing the field of QoE management, focusing in particular on the domain of wireless networks and addressing three management aspects: QoE modeling, monitoring and measurement, and adaptation and optimization. Furthermore, we identify and discuss the key aspects and challenges that need to be considered when conducting research in this area.

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Section: A Survey Of Qoe Monitoring and Measurement Approachesmentioning

confidence: 99%

Survey and Challenges of QoE Management Issues in Wireless Networks

Baraković

Skorin‐Kapov

2013

Journal of Computer Networks and Communications

120

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“…QoE measurement and prediction over time can be beneficial to experts who are interested in understanding how users interact with their services in a long run and to establish factors that may lead to network churn. Further, mobile devices may learn users' QoE over time to perform user-centric handoffs [33].…”

Section: Methods For Qoe Measurement and Prediction Overtimementioning

confidence: 99%

QoE Modelling, Measurement and Prediction: A Review

Mitra¹,

Zaslavsky²,

Åhlund³

2014

Preprint

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In mobile computing systems, users can access network services anywhere and anytime using mobile devices such as tablets and smart phones. These devices connect to the Internet via network or telecommunications operators. Users usually have some expectations about the services provided to them by different operators. Users' expectations along with additional factors such as cognitive and behavioural states, cost, and network quality of service (QoS) may determine their quality of experience (QoE). If users are not satisfied with their QoE, they may switch to different providers or may stop using a particular application or service. Thus, QoE measurement and prediction techniques may benefit users in availing personalized services from service providers. On the other hand, it can help service providers to achieve lower user-operator switchover. This paper presents an extensive review of the state-the-art research in the area of QoE modelling, measurement and prediction. In particular, we investigate and discuss the strengths and shortcomings of existing techniques. Finally, we present future research directions for developing novel QoE measurement and prediction techniques.

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“…One of the major challenges for QoE-aware handoff is accurate QoE prediction for the available wireless networks [5,20]. Further, HANs are prone to stochastic network conditions such as wireless network congestion and wireless signal fading.…”

Section: Introductionmentioning

confidence: 99%

Proactive QoE Provisioning in Heterogeneous Access Networks using Hidden Markov Models and Reinforcement Learning

Mitra,

Åhlund,

Zaslavsky

et al. 2016

Preprint

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Quality of Experience (QoE) provisioning in heterogeneous access networks (HANs) can be achieved via handoffs. The current approaches for QoE-aware handoffs either lack the availability of a network path probing method or lack the availability of efficient methods for QoE prediction. Further, the current approaches do not explore the benefits of proactive QoE-aware handoffs such that user's QoE is maximized by learning from past network conditions and by actions taken by the mobile device regarding handoffs. In this paper, our contributions are two-fold. First, we propose, develop and validate a novel method for QoE prediction based on passive probing. Our method is based on hidden Markov models and Multi-homed Mobility Management Protocol which eliminates the need for additional probe packets for QoE prediction. It achieves the average QoE prediction accuracy of 97%. Second, we propose, develop and validate a novel reinforcement learning based method for proactive QoE-aware handoffs. We show that our method outperforms existing approaches by reducing the number of vertical handoffs by 60.65% while maintaining high QoE levels and by extending crucial functionality such as passive probing mechanisms.

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QoE estimation and prediction using hidden Markov models in heterogeneous access networks

Cited by 9 publications

References 12 publications

Survey and Challenges of QoE Management Issues in Wireless Networks

Survey and Challenges of QoE Management Issues in Wireless Networks

QoE Modelling, Measurement and Prediction: A Review

Proactive QoE Provisioning in Heterogeneous Access Networks using Hidden Markov Models and Reinforcement Learning

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