Online Media QoE Measurement Across Network Path Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for quantifying application quality of experience (QoE) in online media applications are inadequate due to reliance on static formulas and mean opinion score (MOS) values, which fail to accurately reflect user experience, especially in dynamic network conditions where modern codecs adapt to network conditions and aggregate measurements obscure different network degradation patterns.
Innovation Solution
A system that captures media data under specific network path degradation patterns, gathers user feedback, and computes a quality of experience metric to inform predictive configuration changes, using machine learning to identify and predict SLA violations and optimize routing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static formulas and MOS values are used to measure QoE, then measurement simplicity is maintained, but measurement precision deteriorates because MOS values do not accurately reflect true QoE in dynamic network conditions
Solution Approach 1:
The patent transitions from static MOS formulas to dynamic QoE measurement that adapts to changing network conditions. The system continuously monitors network path characteristics and adjusts QoE assessment in real-time, allowing the measurement approach to dynamically respond to varying degradation patterns rather than relying on fixed thresholds
Solution Approach 2:
The patent changes the fundamental parameters used for QoE assessment from aggregate MOS values to pattern-based network path characteristics. By analyzing specific degradation patterns (such as packet loss sequences, latency variations, jitter patterns) rather than averaged metrics, the system achieves more precise QoE measurement that reflects actual user experience
2Measurement precision
If rolling averages are used to aggregate measurements, then computational simplicity is maintained, but measurement precision deteriorates because different network degradation patterns produce similar average values
Solution Approach 1:
The patent segments the aggregated measurement into distinct network path segments and analyzes degradation patterns within each segment separately. Instead of computing a single rolling average across all measurements, the system divides the network path into multiple segments and evaluates QoE for each segment independently, preserving the distinctive characteristics of different degradation patterns
Solution Approach 2:
The patent adds temporal and spatial dimensions to the analysis by examining the sequence and distribution of network measurements across different time points and path segments. This multi-dimensional approach transforms scalar average values into pattern-rich data structures that capture the temporal evolution and spatial distribution of network degradation
3Reliability
If modern codecs with adaptive measures are used, then application robustness improves, but measurement precision deteriorates because corrective measures mask underlying network issues from traditional QoE metrics
Solution Approach 1:
The patent performs preliminary measurement of network path characteristics before applying adaptive codec measures. By capturing the raw network conditions (packet loss, latency, jitter patterns) prior to codec intervention, the system establishes a baseline that reveals the true network degradation patterns, allowing accurate QoE assessment independent of subsequent adaptive corrections
Data Source
AI summary
In one embodiment, a device obtains media data captured by an endpoint of an online application that results from conducting a test in a network that subjects traffic of the online application to a particular network path degradation pattern. The device receives user feedback regarding the media data from one or more user interfaces. The device computes, based on the user feedback, a quality of experience metric for the online application associated with the particular network path degradation pattern. The device causes a configuration change to be made with respect to the online application, based on the quality of experience metric.