VoIP Voice Quality Feedback Triggering and Snapshotting
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Solution Overview
Problem
Current voice quality monitoring systems in VoIP networks lack the capability to detect and record performance issues in real-time, leading to inadequate troubleshooting and user dissatisfaction due to the inability to log detailed call data, which hinders network diagnosis and user reporting of problems.
Innovation Solution
A method and system that allows network nodes to automatically generate triggers for performance monitoring, reconfigure resources, and collect detailed performance metrics during voice calls, enabling instantaneous reporting and snapshotting of network attributes, which can be used for troubleshooting and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice quality monitoring is performed continuously, then measurement precision is improved, but use of energy and network bandwidth increase
Solution Approach 1:
The system performs periodic voice quality monitoring at predetermined intervals rather than continuous monitoring. The performance monitoring component collects voice quality metrics at specific time points during the call, balancing measurement accuracy with reduced network bandwidth consumption and energy usage.
Solution Approach 2:
The system uses existing RTCP feedback mechanisms and embedded performance data in RTP packets to monitor voice quality without requiring additional dedicated monitoring channels. The endpoints themselves generate and report performance metrics, eliminating the need for separate monitoring infrastructure.
2Ease of repair
If detailed performance data is collected and retained, then troubleshooting capability is improved, but device complexity and data storage requirements increase
Solution Approach 1:
The system extracts only the essential performance metrics needed for troubleshooting (voice quality measurements, packet loss rates, jitter values) and separates them from complete raw data. This extraction approach provides sufficient troubleshooting information without requiring storage and processing of all intermediate data, reducing system complexity.
Solution Approach 2:
The system pre-defines the specific performance metrics and data formats to be collected based on anticipated troubleshooting needs. By establishing the monitoring framework and data collection parameters in advance, the system avoids the complexity of dynamic data selection and simplifies the architecture while ensuring all necessary troubleshooting information is captured.
3Ease of operation
If real-time voice quality feedback is provided, then user satisfaction is improved, but response time requirements increase system complexity
Solution Approach 1:
The system combines voice quality monitoring with existing RTCP feedback mechanisms and integrates performance measurement functions into the standard RTP/RTCP protocol stack. By merging monitoring operations with established communication protocols, the system provides real-time user feedback capability without significantly increasing overall system complexity.
Solution Approach 2:
The performance monitoring component is designed to serve multiple functions simultaneously: it collects data for real-time user feedback, generates statistics for network management, provides troubleshooting information, and supports quality of service measurements. This multi-functionality reduces the need for separate dedicated systems for each purpose.
Data Source
AI summary
A system for providing a high communications quality is provided. The system comprises: (a) an input operable to receive a message from at least one of first and second network nodes 200 and 204, the first and second network nodes communicating with one another in a session and the message indicating a service problem with the session and (b) a statistic collection agent 248 operable to cause, in response to the message, at least one of the following operations: (i) reconfiguration of one or more attributes or resources in the network; (ii) variation of a sampling frequency of one or more session-related performance attributes associated with the network; (iii) alteration of the types of session-related performance attributes being collected regarding the network; and (iv) collection of session-related information from nodes other than the at least first and second network nodes.