Video QoE Testing via Network Emulation and Screen Capture
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Solution Overview
Problem
Existing methods for determining quality of user experience (QoE) for video streams lack accuracy in reflecting real-user perceptions, as they often rely on subjective judgments or simplistic models that do not account for varying network conditions.
Innovation Solution
A video QoE testing system that automates the transmission of video streams through network emulators with controlled impairment settings, capturing display screen videos and recording transmission metrics to calculate QoE scores using multiple models, ensuring accurate reflection of user experiences across different transmission conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If subjective judgments or simplistic models are used to determine QoE, then the complexity of the testing system is reduced, but the accuracy of user experience assessment deteriorates
Solution Approach 1:
The patent introduces an intermediary objective model that translates actual network conditions into QoE scores. This model acts as a mediator between the complex reality of network variations and the simplified need for quantitative assessment, providing accurate QoE predictions without requiring complex subjective testing infrastructure
Solution Approach 2:
The patent creates a simplified copy of network conditions through the objective model, which replicates the essential relationships between network parameters and user perception. This copying approach allows accurate QoE assessment using simplified measurements rather than complex subjective testing
2Measurement precision
If multiple QoE models are used to assess video quality under varying network conditions, then the accuracy of QoE scores is improved, but the device complexity increases
Solution Approach 1:
The patent implements dynamic model selection where the appropriate QoE model is automatically chosen based on the current network conditions. The system adapts between different models (e.g., VQM, PSNR, SSIM) depending on whether conditions are good, moderate, or poor, providing accurate assessments without requiring all models to run simultaneously
Solution Approach 2:
The patent changes the operational parameters of the QoE assessment system by selecting different models based on network condition thresholds. This parameter-based selection allows the system to maintain high accuracy across varying conditions while keeping complexity manageable through conditional logic rather than parallel processing
3Reliability
If network emulators with controlled impairment settings are used to simulate various network conditions, then the reliability of QoE assessment is improved, but the device complexity and testing time increase
Solution Approach 1:
The patent performs preliminary configuration of network emulators with pre-defined impairment settings that correspond to specific network conditions (e.g., good, moderate, poor). This preliminary setup allows the system to reliably reproduce specific network scenarios without complex real-time adjustments during testing
4Productivity
If automated transmission testing with multiple models is implemented, then the productivity of QoE assessment is improved, but the device complexity increases
Solution Approach 1:
The patent implements automated self-service functionality where the testing system automatically configures network emulators, executes transmissions under various conditions, collects metrics, and generates QoE reports without manual intervention. This automation increases productivity while the modular architecture manages complexity by separating concerns into independent automated components
Data Source
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AI summary
Techniques for calculating quality of user experience (QoE) scores for video streams as played on receiving devices are described herein. Prior to calculating a QoE score, a device receives a video of a display screen of a receiving device captured while the receiving device plays a video stream. The device also receives transmission metrics from at least one device engaged in the transmission of the video stream to the receiving device. The device then calculates the QoE score for that received video based at least in part on a reference video, the transmission metrics, and one or more QoE models. Additionally, prior to receiving the video or the transmission metrics, the device may automate the transmission of the reference video as the video stream from a sending device to the receiving device over at least one network emulator, including providing network impairment settings to the network emulator.