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QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm.

Ying Bao1, Weimin Lei1, Wei Zhang1

  • 1Institute of Communication and Information System, School of Computer Science and Engineering, Northeastern University, Shenyang, 110819 China.

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|July 12, 2016
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Summary
This summary is machine-generated.

This study introduces a fuzzy clustering heuristic algorithm for network media transmission. It improves Quality of Experience (QoE) evaluation by calculating service scores at the server, enhancing user satisfaction.

Keywords:
End-to-end QoE evaluation systemMultipath transmissionQuality of experience (QoE)Quality of service (QoS)

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Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Improving Quality of Experience (QoE) is crucial for network media transmission services.
  • Accurate QoE evaluation is essential for optimizing transmission control mechanisms.

Purpose of the Study:

  • To propose a novel QoE collaborative evaluation method using a fuzzy clustering heuristic algorithm.
  • To enhance service score calculation at the server side for improved QoE.

Main Methods:

  • Server-side collection of Quality of Service (QoS) parameters, node location, and user feedback.
  • Utilizing big data processing for historical data management and heuristic rule-based user score prediction.
  • Implementing fuzzy clustering analysis to generate QoE scores and management messages.

Main Results:

  • The proposed method effectively calculates service-based QoE scores.
  • Simulation experiments validated the effectiveness of the fuzzy clustering heuristic algorithm for QoE evaluation.
  • Generated management messages provide valuable feedback to clients.

Conclusions:

  • The fuzzy clustering heuristic algorithm offers an effective approach for QoE collaborative evaluation.
  • Server-side QoE score calculation enhances network media transmission services.
  • This method provides a robust framework for optimizing QoE in real-time network environments.