A survey of data-driven approach on multimedia QoE evaluation

Huang, Ruochen; Wei, Xin; Zhou, Liang; Lv, Chaoping; Jin, Jiefeng

doi:10.1007/s11704-018-6342-7

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…Conventional QoE assessment ( Figure 1 ) typically considers service quality metrics complemented by subjective user evaluations through surveys or interviews to gauge their perception of the service. In contrast, data-driven QoE assessment leverages advanced analytics and machine learning techniques to analyze large datasets, including data on system, human and context influence factors ( Huang et al, 2018 ). This approach involves extracting insights from service quality data, user behavior, feedback, and operational data, providing a more dynamic and real-time understanding of QoE, often eliminating the need for explicit user input, and allowing for continuous improvement in service delivery based on data-driven insights.…”

Section: Literature Reviewmentioning

confidence: 99%

An empirical assessment of the use of an algorithm factory for video delivery operations

Molnar,

Ferreira Pires,

de Boer

et al. 2024

Front. Artif. Intell.

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IntroductionVideo service providers are moving from focusing on Quality of Service (QoS) to Quality of Experience (QoE) in their video networks since the users’ demand for high-quality video content is continually growing. By focusing on QoE, video service providers can provide their subscribers with a more personalized and engaging experience, which can help increase viewer satisfaction and retention. This focus shift requires not only a more sophisticated approach to network management and new tools and technologies to measure and optimize QoE in their networks but also a novel approach to video delivery operations.MethodsThis paper describes the components, interactions, and relationships of an algorithm factory for video delivery operation that assures high QoE for video streaming services. The paper also showcases the results of gradually implementing an algorithm factory in the video industry. Using a dataset from 2016 to 2022, we present the case of a European PayTV service provider that achieved improved performance measured by both objective and subjective metrics.ResultsThe use of an algorithm factory significantly improved the PayTV service provider’s performance. The study found a fivefold increase in the speed of critical incident resolution and a 59% reduction in the number of critical incidents, all while expanding the customer base and maintaining the same level of labor resources. The case also demonstrates a strong positive relation between the productivity measures of the PayTV operator and their survey-based quality ratings. These results underscore the importance of flawless QoS and operational excellence in delivering QoE to meet the evolving demands of viewers.DiscussionThe paper adds to the existing literature on relationships between operational efficiency, innovation, and subjective quality. The paper further offers empirical evidence from the PayTV industry. The insights provided are expected to benefit both traditional and over-the-top (OTT) video service providers in their quest to stay ahead in the rapidly evolving video industry. It may also translate to other service providers in similar industries committed to supporting high-quality service delivery.

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Section: Literature Reviewmentioning

confidence: 99%

An empirical assessment of the use of an algorithm factory for video delivery operations

Molnar,

Ferreira Pires,

de Boer

et al. 2024

Front. Artif. Intell.

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“…In recent years, there has also been a growing interest in the classi ication of end users' perceived QoE when using online media apps, such as video streaming services [13]. A survey by Huang et al provided an overview of the state-of-the-art data-driven approaches for QoE evaluation [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing user experience in home networks with machine learning-based classification

Rushat Rai,

Thomas Basikolo

2024

ITU J-FET

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With the rapid development of mobile Internet, home broadband has been integrated into people's daily lives, and the market has become increasingly saturated. User experience and broadband quality have become the key factors determining market competitiveness, and consequently, most operators currently are increasing attention to network quality issues and how to improve user experience. This paper proposes an efficient machine learning model to accurately evaluate home user network experiences. The dataset used encompasses network indicator data from 500 anonymized users, and presents a set of formidable challenges including a non-standard sampling rate and time range, an uneven distribution of observations, multiple recorded observations for identical timestamps, a constrained sample size, a subjective definition of Internet experience, and a lack of essential information regarding the data collection setup. Our novel time series characteristic-based method extracts thousands of descriptive statistics from the time series sequences which reveal that, even in the face of the dataset's inherent complexities, our proposed method excels, achieving an impressive 67% validation accuracy. This represents a substantial 3% enhancement over the performance of conventional models on this dataset. Furthermore, we explore the potential of a Recurrent Neural Network (RNN) model, which also yields promising results with a validation accuracy of 58%. It is important to underscore that the performance of the RNN model could be substantially enhanced with a larger dataset. [...]

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“…Recently, data analysis is recognized as a measurement approach [18][19][20][21][22][23][24][25][26][27] to overcome the challenges of traditional methods. Tis approach is made plausible through the use of machine learning and metadata produced by networks [13].…”

Section: Qoe Management In Multimedia Hetnet Networkmentioning

confidence: 99%

Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

Omar,

Justin P.,

Windmi Jonathan M.

et al. 2024

Journal of Computer Networks and Communications

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This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the benefits of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents technical challenges, including accurately measuring QoE and managing the complexity of heterogeneous networks. To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. Ultimately, the use of QoE in the optimization of heterogeneous multimedia networks contributes to a substantial improvement in the quality of multimedia services offered to end users.

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A survey of data-driven approach on multimedia QoE evaluation

Cited by 9 publications

References 40 publications

An empirical assessment of the use of an algorithm factory for video delivery operations

An empirical assessment of the use of an algorithm factory for video delivery operations

Enhancing user experience in home networks with machine learning-based classification

Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

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