Real-Time Voice QoE Prediction from Encrypted IP Flow Metadata
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Solution Overview
Problem
Mobile communication networks struggle to accurately infer the quality of real-time conversational voice applications like WebRTC due to lack of network traffic information, especially for latency-sensitive content, leading to inadequate resource allocation and failure to meet Service Level Agreements (SLAs).
Innovation Solution
An apparatus in the mobile communication network combines information from monitored IP flows and application behavior to predict Quality of Experience (QoE) and Quality of Service (QoS) by estimating late loss and using machine learning algorithms, leveraging QUIC protocol features like the spin bit for real-time predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the mobile communication network monitors IP flows and uses machine learning algorithms to predict QoE/QoS, then the measurement precision of voice quality is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a dedicated network apparatus (NWDAF in 5G Core) as an intermediary component that performs QoE prediction by collecting network measurements and application metadata. This mediator architecture separates the prediction function from the core network elements, allowing accurate voice quality assessment without requiring every network node to implement complex analysis capabilities locally.
Solution Approach 2:
The patent divides the QoE prediction system into separate functional modules: network measurement collection, application metadata gathering, machine learning prediction engine, and result delivery. This segmentation allows each component to be optimized independently and simplifies deployment by distributing complexity across specialized functions rather than requiring monolithic complex systems throughout the network.
2Reliability
If the network uses end-to-end encryption for WebRTC applications, then the security and privacy are improved, but the network's ability to monitor traffic characteristics deteriorates
Solution Approach 1:
Instead of attempting to decrypt all encrypted traffic (which would compromise security), the patent applies partial action by focusing monitoring on unencrypted network-level metadata such as packet arrival times, inter-packet gaps, and flow characteristics. This approach gains sufficient QoE prediction capability without requiring decryption of the actual voice content, thus maintaining encryption security while obtaining necessary monitoring information.
Solution Approach 2:
The patent extracts relevant QoE prediction information from the network layer metadata that remains visible even under encryption. By taking out and analyzing only the necessary network characteristics (timing, packet loss indicators, throughput) rather than attempting to access application-layer encrypted data, the system maintains security while obtaining sufficient monitoring capabilities for accurate voice quality assessment.
3Productivity
If IP flows carrying speech samples are merged into a single transport connection, then the network efficiency is improved, but the ability to identify and monitor individual voice flows deteriorates
Solution Approach 1:
The patent applies local quality analysis by examining specific characteristics of packets within the merged flow that are indicative of voice traffic. By analyzing local patterns such as packet inter-arrival times, payload sizes, and timing patterns that are unique to voice codecs, the system can identify and monitor individual voice flows even within a multiplexed connection, maintaining network efficiency while enabling flow-specific monitoring.
Solution Approach 2:
The patent uses metadata indicators as analogs to color changes - distinct characteristics (timing patterns, packet structure features) that allow the system to visually distinguish voice flows from other traffic types within the merged connection. By detecting these distinctive metadata 'signatures', the system can identify individual voice flows without requiring separate physical or logical connections, thus maintaining multiplexing efficiency while enabling individual flow monitoring.
4Ease of operation
If the network lacks collaborative solutions with application providers, then the network autonomy is improved, but the quality control capability deteriorates
Solution Approach 1:
The patent creates a universal QoE prediction platform that serves multiple functions: it collects network measurements, processes application metadata from various providers, runs prediction algorithms, and delivers quality assessments to both network operators and application providers. This multi-functional system enables autonomous network operation while maintaining collaborative capabilities through standardized interfaces that can work with different application providers without requiring custom integrations for each.
Data Source
AI summary
An apparatus in a mobile communication network combines information from monitoring IP flows carrying latency sensitive content passing the apparatus and information about the application behavior and target Quality of Experience (QoE) or target connectivity characteristics such as Quality of Service (QoS) from the application to provide ongoing predictions of QoE/QoS. In some cases, the apparatus exploits a probe on a device to generate traffic for learning flow characteristics not obtained from monitoring application IP flows in the network. Embodiments disclosed herein can be used to predict quality metrics for many applications where jitter/latency is a factor affecting perceived quality, such as QoE for a human consumer or QoS for machine type communications. The embodiments are applicable to the analysis of traffic carrying conversational speech.