Quality of experience management in mobile content delivery systems

Agboma, Florence; Liotta, Antonio

doi:10.1007/s11235-010-9355-6

Cited by 69 publications

(49 citation statements)

References 16 publications

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“…The influence of QoS parameters, i.e., network related aspects, such as throughput, packet delay, or packet loss, on the QoE during VoIP calls has been studied extensively [7,14]. The de-gree of influence of QoS parameters on user perception has been determined and enabled the development of prediction models for multimedia services on mobile devices [2]. For VoIP calls, packet losses and the pattern of the packet losses, i.e., the distribution and duration of packet loss runs, has a significant influence on the perceptual quality of the conversation [3].…”

Section: Related Workmentioning

confidence: 99%

Quality assessment and usage behavior of a mobile voice-over-IP service

et al. 2015

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Voice-over-IP (VoIP) services offer users a cheap alternative to the traditional mobile operators to make voice calls. Due to the increased capabilities and connectivity of mobile devices, these VoIP services are becoming increasingly popular on the mobile platform. Understanding the user's usage behavior and quality assessment of the VoIP service plays a key role in optimizing the Quality of Experience (QoE) and making the service to succeed or to fail.By analyzing the usage and quality assessments of a commercial VoIP service, this paper identifies device characteristics, context parameters, and user aspects that influence the usage behavior and experience during VoIP calls. Whereas multimedia services are traditionally evaluated by monitoring usage and quality for a limited number of test subjects and during a limited evaluation period, this study analyzes the service usage and quality assessments of more than thousand users over a period of 120 days. This allows to analyze evolutions in the usage behavior and perceived quality over time, which has not been done up to now for a widelyused, mobile, multimedia service. The results show a significant evolution over time of the number of calls, the call duration, and the quality assessment. The time of the call, the used network, and handovers during the call showed to have a significant influence on the users' quality assessments.Keywords usage behavior · user experience · voiceover-ip · mobile · quality assessment 1 Introduction

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Section: Related Workmentioning

confidence: 99%

Quality assessment and usage behavior of a mobile voice-over-IP service

et al. 2015

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“…[21] Wireless Network [12], [26], [27], [28] [18], [19] [20], [29], [30] Next Generation Network [15] [22]…”

Section: Research Issuementioning

confidence: 99%

“…[31], [32] derstanding a relationship between QoS and QoE [12], [26]- [28]. Agboma et al [26] studied QoE models for different types of multimedia contents delivered onto mobile terminals and found QoS and QoE correlations that were dependent on terminals and multimedia content types.…”

Section: Research Issuementioning

confidence: 99%

“…Agboma et al [26] studied QoE models for different types of multimedia contents delivered onto mobile terminals and found QoS and QoE correlations that were dependent on terminals and multimedia content types. To understand QoE assessment for mobile broadband scenarios, Reichl et al [27] found basic relationships between QoS and QoE which can be described by the Weber-Fechner law used in psychophysics.…”

Section: Research Issuementioning

confidence: 99%

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Quality-of-Experience (QoE) in Emerging Mobile Social Networks

Dong

Kimata

Sugiura

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYMobile social networks (MSN) provides diverse services to meet the needs of mobile users, i.e., discovering new friends, and sharing their pictures, videos and other information among their common interest friends. On the other hand, Quality-of-Experience (QoE) is a new concept related to but differs from Quality-of-Service (QoS) perception. QoE is a subjective measure of a customer's experiences with a service focuses on the entire service experience, and is a more holistic evaluation. So far, QoS issues have been focused and mainly addressed in the literature of MSNs.

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“…In this situation, assessing accurately and in realtime the network performance becomes fundamental [2] [3]. Traditionally, Quality-of-Service (QoS) based evaluations have been used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Cognitive no-reference video quality assessment for mobile streaming services

Vega

Giordano

Mocanu

et al. 2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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Abstract-The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experience (QoE), entails the use of automated methods (which excludes subjective QoE) that can be executed in real-time (i.e. without delaying the streaming process). This calls for lightweight algorithms that provide accurate results under considerable constraints. Starting from a low complexity no-reference objective algorithm for still images, in this work we contribute a new version that not only works for videos but, is general enough to adjust to a diverse range of video types while not significantly increasing the computational complexity. To achieve the necessary level of flexibility and computational efficiency, our method relies merely on information available at the client side and is equipped with a lightweight Artificial Neural Network which makes the algorithm independent from type of network or video. Its resource efficiency and generality make our method fit to be used in mobile streaming services. To prove the viability of our approach, we show a high level of correlation with the well-known full-reference method SSIM.

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Quality of experience management in mobile content delivery systems

Cited by 69 publications

References 16 publications

Quality assessment and usage behavior of a mobile voice-over-IP service

Quality assessment and usage behavior of a mobile voice-over-IP service

Quality-of-Experience (QoE) in Emerging Mobile Social Networks

Cognitive no-reference video quality assessment for mobile streaming services

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