XR QoE Mapping for Roundtrip Delay Adjustment
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Solution Overview
Problem
Existing extended reality (XR) applications face challenges in maintaining a quality of experience (QoE) due to inconsistent and unoptimized roundtrip delays, which affect the rendering process across various devices.
Innovation Solution
A system and method for adjusting roundtrip delays in XR applications by configuring a delay adjustment mechanism with defined QoE levels, dependency information, and an adjustment strategy, using a QoE map to determine optimal rendering qualities and minimize delays through exponential moving averages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If different QoE levels are defined for each object with dependency information, then the quality of experience can be optimized, but the device complexity increases
Solution Approach 1:
The system segments the XR scene into multiple objects, each with independent QoE levels and processing delays. The dependency information graph further segments the relationships between objects, allowing selective optimization of individual objects without affecting the entire scene. This segmentation enables fine-grained control of rendering quality while managing overall system complexity.
Solution Approach 2:
The adjustment mechanism dynamically modifies QoE levels based on measured roundtrip delays and elementary task processing delays. The system adapts the rendering quality of individual objects in real-time according to performance metrics, transforming a static rendering system into a dynamic one that self-adjusts to maintain acceptable QoE under varying conditions.
2Reliability
If roundtrip delay adjustment is performed frequently to maintain acceptable delay values, then the quality of experience improves, but the processing time and computational overhead increase
Solution Approach 1:
The system implements a feedback mechanism that measures roundtrip delays and elementary task processing delays, compares them against threshold values, and adjusts QoE levels accordingly. This closed-loop feedback ensures that adjustments are data-driven and only performed when necessary, avoiding unnecessary processing while maintaining acceptable performance.
Solution Approach 2:
Instead of adjusting all objects uniformly, the system applies partial adjustments only to specific objects that exceed delay thresholds. The dependency information graph enables selective adjustment of individual objects or groups of objects, avoiding the excessive action of adjusting the entire scene when only局部 adjustments are needed.
3Measurement precision
If multiple delay measurements are collected and averaged to improve accuracy, then the measurement precision improves, but the time required for measurement increases
Solution Approach 1:
The system performs delay measurements periodically rather than continuously, collecting multiple samples at regular intervals. This periodic sampling approach improves measurement precision through averaging while limiting the total measurement time to acceptable thresholds, balancing accuracy requirements with real-time performance needs.
Data Source
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AI summary
A system and method for adjusting roundtrip delays of an extended reality (XR) application while maintaining a quality of experience (QoE) are disclosed. The system includes a processor and memory. The system and method include configuring a roundtrip delay adjustment mechanism including at least one of different QoE levels defined for each object, dependency information between QoE level of objects, and an adjustment strategy; measuring at least one roundtrip delay and at least one elementary task processing delay including calculating the at least one roundtrip delay based on several samples; and determining an appropriate set of QoE levels based on a QoE map and the configured adjustment mechanism.