QoE Measurement Using Embedded Meta-Commands for IP Network Testing
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Solution Overview
Problem
Current methods for measuring Quality of Experience (QoE) in IP networks are limited in accurately assessing user experience across various activities and fail to realistically emulate user behavior, leading to inadequate testing of edge devices and network performance.
Innovation Solution
A method for establishing a Quality of Experience score for user activities over IP networks using a combination of objective and subjective metrics, with embedded meta-commands in normal network packets to synchronize test control information, allowing for realistic emulation of user behavior and improved network testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional QoE measurement methods are used, then measurement simplicity is maintained, but measurement precision is insufficient for accurately assessing user experience across various activities
Solution Approach 1:
The patent segments QoE measurement into multiple independent components: objective metrics collection from network packets, subjective metrics collection from user feedback, behavior profile definitions for different user activities, and separate analysis modules for each metric type. This segmentation allows precise measurement of each aspect while keeping individual components manageable in complexity.
Solution Approach 2:
The patent introduces intermediary elements including behavior profiles that mediate between raw network data and QoE assessment, and a centralized QoE analysis server that mediates between distributed metric collection points and final evaluation. These intermediaries structure the complexity while enabling comprehensive measurement.
2Reliability
If realistic user behavior emulation is implemented, then testing reliability is improved, but device complexity increases due to multiple metrics and synchronization requirements
Solution Approach 1:
The patent applies preliminary action by pre-defining behavior profiles that capture realistic user activity patterns before actual testing begins. These profiles include predefined sequences of network activities, timing characteristics, and interaction patterns that emulate real users. This preparation enables reliable testing without requiring complex real-time user simulation during execution.
Solution Approach 2:
The patent creates simplified copies of real user behavior through behavior profiles that replicate essential characteristics of user activities without requiring actual users or complex simulation engines. These behavioral copies capture the essence of realistic usage patterns while maintaining manageable system complexity.
3Adaptability or versatility
If multiple objective and subjective metrics are collected, then QoE assessment comprehensiveness is improved, but loss of information increases due to the complexity of synthesizing multiple data sources
Solution Approach 1:
The patent segments the synthesis process into distinct stages: objective metrics are processed separately from subjective metrics, each type is analyzed according to its specific characteristics, and then results are integrated through defined weighting and aggregation rules. This segmented approach prevents information loss by maintaining the integrity of each metric type while achieving comprehensive assessment.
Solution Approach 2:
The patent transforms multiple diverse metrics into a unified QoE score through parameter changes including normalization of different metric scales, application of activity-specific weighting factors, and aggregation into standardized output formats. These transformations preserve essential information while enabling versatile comparison and assessment across different activity types.
Data Source
AI summary
An embodiment of the present invention provides a method for establishing Quality of Experience (QoE) measurements and metrics for different types of actual user activities over IP networks. These activities include, but are not limited to web browsing, sending and receiving email, file downloading and uploading, peer to peer (P2P) networking, VoIP, online gaming, and media streaming. The measurement of the QoE metrics is based on both objective and subjective metrics, including network characteristics, such as packet loss and latency, along with empirical observations of the user activities.