XR Rendering Stack Adaptation for Network QoS
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Solution Overview
Problem
Extended reality (XR) interactions face network quality of service (QoS) issues, leading to gaps in rendering quality and user experience due to varying network conditions, which conventional systems fail to address effectively.
Innovation Solution
A system utilizing 3D scanning data from XR engagements to determine network QoS issues by receiving current and historical environment condition information, adjusting rendering resources between cloud, edge, and local devices to meet performance thresholds, and sending notifications for optimal rendering adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If rendering quality is increased to improve user experience, then visual quality and detail are improved, but network bandwidth consumption increases and network stability deteriorates
Solution Approach 1:
The system dynamically adjusts rendering quality parameters based on real-time network condition assessments. The rendering stack reconfigures itself by modifying rendering quality levels, object resolution, and data transmission rates according to current network performance, enabling adaptive balance between visual quality and network stability.
Solution Approach 2:
The system changes key rendering parameters such as resolution, frame rate, and detail level based on network conditions. By modifying these parameters dynamically, the system optimizes the trade-off between rendering quality and network resource consumption, ensuring stable XR experience under varying network conditions.
2Productivity
If network bandwidth is increased to maintain high rendering quality, then rendering performance is improved, but network cost and energy consumption increase
Solution Approach 1:
The system applies partial rendering quality rather than maximum quality consistently. By assessing network conditions and applying only the necessary rendering quality level needed for acceptable user experience, the system avoids excessive energy consumption and network resource usage while maintaining adequate performance.
Solution Approach 2:
The system dynamically adjusts rendering parameters to match actual network capabilities and user experience requirements. This ensures that energy and network resources are allocated efficiently, avoiding waste from over-rendering when network conditions or user needs justify lower quality levels.
3Adaptability or versatility
If rendering quality is dynamically adjusted to adapt to network conditions, then network adaptability is improved, but system complexity increases
Solution Approach 1:
The rendering stack is segmented into modular components that can be independently configured and adjusted. This modular architecture enables network-adaptive rendering by allowing selective modification of specific rendering parameters and components without requiring complete system redesign, thus managing complexity while achieving adaptability.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor network conditions and user experience metrics. This feedback drives automatic adjustments to rendering quality, reducing the need for complex manual configuration and enabling intelligent adaptation through closed-loop control.
Data Source
AI summary
A method may include receiving current environment condition information associated with an extended reality device; receiving historical environment condition information associated with the extended reality device; based on current environment condition information and the historical environment condition information, determining one or more adjustments to meet a performance threshold for rendering objects on the an extended reality device or using the an extended reality device; and sending instructions to implement the one or more adjustments to meet the performance threshold for rendering objects on the extended reality device or using the extended reality device.