Distributed XR Rendering Coordination for Latency Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for managing extended reality (XR) data face challenges in efficient rendering and resource management, leading to suboptimal user experiences due to high computational and network resource demands, particularly in rendering high-priority XR data.
Innovation Solution
The proposed solution involves a coordination technique for disjointed and distributed rendering of XR data, where primary nodes manage rendering information and allocate tasks to various nodes within the network, including edge, near-edge, and far-edge servers, to optimize resource usage and reduce latency by prioritizing high-priority data rendering closer to user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If XR data is rendered using centralized cloud computing, then rendering power is sufficient, but network latency increases and computational resources are over-provisioned
Solution Approach 1:
The rendering system is segmented into multiple hierarchical levels (cloud rendering nodes, edge computing nodes, and device-level renderers). Different portions of XR data are rendered at different locations based on priority and requirements, dividing the centralized workload into distributed segments that reduce latency for high-priority content while maintaining cloud resources for less time-sensitive rendering tasks.
Solution Approach 2:
Different rendering qualities and locations are assigned to different XR data based on their priority and requirements. High-priority XR data is rendered at locations closer to the user device (edge or device-level), while lower-priority data can be rendered at remote cloud nodes, creating a non-uniform quality distribution that optimizes both latency and resource utilization.
2Device complexity
If all XR data is rendered at the same location and priority, then rendering coordination is simple, but resource efficiency decreases
Solution Approach 1:
XR data is segmented into different priority levels and rendered at different locations based on their characteristics. The system manages this segmentation through coordination information that tracks which nodes are responsible for rendering which data portions, maintaining organizational simplicity despite the distributed architecture.
Solution Approach 2:
The rendering architecture is dynamic rather than static, allowing the system to adaptively assign rendering tasks to different nodes based on current network conditions, node availability, and data priority requirements. This dynamic flexibility improves resource efficiency without requiring complex predetermined coordination protocols.
3Productivity
If high-priority XR data is rendered at remote locations, then resource utilization is optimized, but rendering latency increases
Solution Approach 1:
High-priority XR data is rendered at locations closer to the user device (edge computing nodes or device-level renderers) to minimize latency, while lower-priority data can be rendered at more remote cloud locations. This creates a local quality advantage for time-sensitive content without sacrificing overall resource utilization efficiency.
Solution Approach 2:
The rendering workload is segmented by priority levels, with high-priority tasks assigned to nearby nodes for fast rendering and lower-priority tasks assigned to remote nodes for efficient resource utilization. This segmentation allows the system to optimize both latency for critical data and overall productivity without compromise.
4Power
If mobile devices have higher computational power, then local rendering quality improves, but device form factor and power consumption increase
Solution Approach 1:
Edge computing nodes serve as intermediaries between the mobile device and the cloud rendering nodes. These intermediary nodes perform rendering tasks that would otherwise require significant computational power in the mobile device, allowing high-quality XR rendering without requiring the device itself to have powerful hardware.
Solution Approach 2:
The computational workload is segmented between the mobile device, edge nodes, and cloud nodes. The device handles only essential local rendering tasks, while more computationally intensive tasks are offloaded to edge and cloud infrastructure, maintaining device portability and battery life while achieving high rendering quality.
Data Source
AI summary
Techniques, devices, and systems for computer-altered reality data rendering coordination are discussed herein. Coordination of disjointed and/or distributed rendering of computer-altered reality data (e.g., extended reality (XR) data) performed by networks and systems can be performed. The coordination can be performed based on various types of information. Information utilized to perform the coordination of computer-altered reality data rendering can include coordination and/or rendering information (e.g., global coordination information, global rendering information, other global information of one or more other types, or any combination thereof). The computer-altered reality data can be rendered utilizing coordinated nodes, which can provide rendered computer-altered reality data for the user devices.