A Quality of Experience Management Framework for Mobile Users

Algar, María Jesús; Diego, Isaac Martín de; Fernández-Isabel, Alberto; Monjas, Miguel Ángel; Ortega, Felipe; Moguerza, Javier M.; Jacynycz, Hektor

doi:10.1155/2019/2352941

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Similarly, Wassermann et al [72] suggested that YouTube mobile QoE can be realized via machine learning models (e.g., random forests) with high accuracy using only network-related features. In turn, Algar et al [73] proposed an automated realtime video QoE management framework for mobile users depending on the network traffic policies and user actions. Besides above wroks, Mrvelj and Matulin [74] studied user's QoE in real life environments by examining the impact of packet loss related issues in User Datagram Protocol (UDP) when 1-h multimedia content (e.g., a TV program) were streamed.…”

Section: A Influencing Factorsmentioning

confidence: 99%

Why is Multimedia Quality of Experience Assessment a Challenging Problem?

et al. 2019

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Quality of experience (QoE) assessment occupies a key role in various multimedia networks and applications. Recently, large efforts have been devoted to devise objective QoE metrics that correlate with perceived subjective measurements. Despite recent progress, limited success has been attained. In this paper, we provide some insights on why QoE assessment is so difficult by presenting few major issues as well as a general summary of quality/QoE formation and conception including human auditory and vision systems. Also, potential future research directions are described to discern the path forward. This is an academic and perspective article, which is hoped to complement existing studies and prompt interdisciplinary research. INDEX TERMS Audiovisual perception, data-driven analysis, multimedia quality, objective quality metric, quality of experience, quality of services (QoS).

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Section: A Influencing Factorsmentioning

confidence: 99%

Why is Multimedia Quality of Experience Assessment a Challenging Problem?

et al. 2019

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“…• Slice provisioning (instantiation) may utilize well-defined usage types through clustering, in order to select appropriate templates for the slices based on predicted requirements [3]. • Quality of Experience (QoE) estimation tries to map explicitly measurable Key Performance Indicators (KPIs), such as network delay, jitter or throughput to user satisfaction levels [4]. Here, clustering may be utilized to establish user archetypes.…”

Section: Introductionmentioning

confidence: 99%

Decorrelating Adversarial Nets for Clustering Mobile Network Data

Kajo¹,

Schnellbach²,

Mwanje³

et al. 2021

Preprint

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Deep learning will play a crucial role in enabling cognitive automation for the mobile networks of the future. Deep clustering, a subset of deep learning, could be a valuable tool for many network automation use-cases. Unfortunately, most state-of-the-art clustering algorithms target image datasets, which makes them hard to apply to mobile network data due to their highly tuned nature and related assumptions about the data. In this paper, we propose a new algorithm, Decorrelating Adversarial Nets for Clustering-friendly Encoding (DANCE), intended to be a reliable deep clustering method which also performs well when applied to network automation use-cases. DANCE uses a reconstructive clustering approach, separating clustering-relevant from clustering-irrelevant features in a latent representation. This separation removes unnecessary information from the clustering, increasing consistency and peak performance. We comprehensively evaluate DANCE and other select state-of-the-art deep clustering algorithms, and show that DANCE outperforms these algorithms by a significant margin on a mobile network dataset.

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