Streaming Quality Metric via Session Metadata Analysis
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Solution Overview
Problem
Existing methods for estimating the quality of streaming sessions are subjective, unreliable, and biased, relying on user feedback that is sparse and inaccurate, which hinders objective measurement and improvement of user experience in cloud-based streaming services.
Innovation Solution
A system and method that analyzes performance metadata from various components of a streaming pipeline to calculate a quality metric, incorporating timestamps and weights for components like stutter, latency, and picture quality, enabling objective evaluation and automatic adjustment of pipeline parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective user feedback is collected to estimate streaming quality, then user satisfaction can be measured, but the feedback is sparse, biased, and unreliable
Solution Approach 1:
The patent replaces the mechanical system of subjective user feedback collection with an automated objective measurement system that uses metadata analysis and machine learning models to calculate quality metrics, eliminating human bias and sparsity issues
Solution Approach 2:
The patent introduces metadata and machine learning models as intermediaries between the streaming service and quality assessment, using these intermediate data structures to objectively represent user experience without direct user input
2Productivity
If user feedback is solicited frequently to improve measurement density, then more data is collected, but users become annoyed and feedback quality decreases
Solution Approach 1:
The system performs self-service quality assessment by automatically collecting metadata and calculating quality metrics without requiring user intervention, eliminating the need to prompt users for feedback while maintaining continuous measurement capability
3Loss of time
If pipeline parameters are adjusted to reduce latency, then user experience improves, but network congestion and rendering complexity increase
Solution Approach 1:
The patent implements dynamic adjustment of pipeline parameters based on real-time quality metric calculations, allowing the system to adaptively optimize latency, resolution, and bitrate according to current network conditions and user experience patterns
4Measurement precision
If objective quality metrics are implemented, then measurement accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the quality assessment system into distinct modular components: metadata collection modules, processing modules for different metadata types, quality metric calculation modules, and pipeline adjustment modules, allowing independent development and maintenance of each function
Data Source
AI summary
A technique for evaluating qualitative streaming experience using session performance metadata is disclosed herein. A pipeline of a streaming service can be adapted to collect metadata, such as timestamps, from various components of the pipeline. The metadata can then be analyzed to calculate an objective quality metric for each streaming session using weighted scores derived from the metadata for a plurality of different components including, but not limited to, stutter, latency, and/or picture quality. The quality metric is designed to have high correlation with subjective measures of quality by users of the streaming service, but provides dense data samples compared to typical sparse responses collected from user feedback (e.g., user surveys). The objective quality metric can be utilized to quickly adjust, either manually or automatically, the streaming service parameters to improve the quality of the streaming service due to changes in, e.g., streaming content.