Fault Localization via QoE Indicators in IPTV Networks
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Solution Overview
Problem
Current fault localization methods in IPTV systems are inefficient and inaccurate, leading to delayed identification and resolution of faulty network devices, which negatively impacts user experience.
Innovation Solution
A method and device that utilize user experience data, network topology data, and resource management data to determine Quality of Experience (QoE) indicators, identifying questionable devices through distribution characteristic analysis and similarity aggregation, thereby improving fault localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual fault localization is used through user complaints and maintenance department troubleshooting, then the process is simple to operate, but the fault localization is excessively delayed and operations are complicated
Solution Approach 1:
The system performs preliminary actions by continuously monitoring user experience data, network topology data, and resource management data to calculate QoE indicators and identify questionable devices before faults actually occur. This proactive approach eliminates the need for waiting for user complaints and manual troubleshooting, significantly reducing fault localization time while maintaining automated operation.
Solution Approach 2:
The system enables self-service by automatically collecting and analyzing various data sources, calculating QoE indicators, identifying questionable devices through distribution characteristic analysis, and locating faults without requiring manual intervention. The automated fault localization system serves itself by continuously monitoring and analyzing network status, eliminating the need for maintenance department involvement in the detection phase.
2Measurement precision
If QoS indicator monitoring is used to locate faulty devices, then the monitoring process is automated, but the accuracy of fault localization is low because QoS exceptions may not cause deterioration of final user experience
Solution Approach 1:
The system changes the monitoring parameter from traditional QoS indicators (packet loss rate, delay) to QoE indicators calculated from user experience data. This parameter change directly addresses the inaccuracy of QoS-based fault localization by focusing on actual user experience metrics that directly reflect service quality perceived by users, thereby improving fault localization accuracy while maintaining automated monitoring.
Solution Approach 2:
The system implements feedback by continuously collecting user experience data, calculating QoE indicators, comparing them against thresholds, and using the results to identify questionable devices. This closed-loop feedback mechanism ensures that fault localization is based on actual user experience impacts rather than intermediate QoS metrics, improving accuracy while maintaining full automation through automated data collection, analysis, and alert generation.
3Reliability
If traditional fault localization methods are used, then the system complexity is low, but the user experience deterioration is not accurately reflected and fault identification is delayed
Solution Approach 1:
The system merges multiple data sources including user experience data, network topology data, and resource management data into a unified fault localization framework. By combining these diverse data streams and integrating them with QoE indicator calculation and distribution characteristic analysis, the system achieves accurate reflection of user experience deterioration while managing complexity through systematic integration rather than isolated monitoring components.
Solution Approach 2:
The system adds another dimension to fault localization by incorporating user experience data and QoE indicators as a new dimension beyond traditional network performance metrics. This dimensional expansion allows the system to accurately reflect user experience deterioration by considering both network-side metrics and user-perceived quality, achieving higher reliability while managing complexity through structured multi-dimensional analysis.
Data Source
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Figure 3B
AI summary
Embodiments of the present invention relate to a fault localization method and device. The method includes: obtaining user experience data, network topology data, and resource management data that are of a video service; where the network topology data is used to represent a connection relationship between network devices, and the resource management data is used to represent a connection relationship between user equipment and the network devices; determining a quality of experience QoE experience indicator of a network device based on the user experience data, the network topology data, and the resource management data, where the QoE experience indicator of the network device are determined based on user experience data of user equipment served by the network device; and when quality of experience represented by the QoE experience indicator of the network device is lower than quality of experience represented by a device screening threshold, determining the network device as a possible questionable device. According to the embodiments of the present invention, accuracy of fault localization is high.