Cross-Reality Session Resource Optimization
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Solution Overview
Problem
Cross-reality environments face computational and bandwidth-intensive challenges, leading to delays and performance issues due to the need for rendering complex scenes and environments, which are not optimized for resource usage.
Innovation Solution
A system and method that optimize resource usage in cross-reality sessions by determining the level of detail for entities based on expected interactions and resource availability, allowing for progressive loading and rendering of environments, with the server computer generating optimized session data and updating rendering levels based on user attention and interaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex environments are pre-loaded in cross-reality sessions, then users can interact with various aspects of the environment, but this causes waiting time, network delays, and intensive resource usage
Solution Approach 1:
The system performs preliminary actions by pre-defining trigger points and potential entity locations in the environment, but only renders entities when triggered by user actions. This allows the environment to be ready for interaction without pre-rendering all entities, reducing waiting time while maintaining interaction capability.
Solution Approach 2:
The environment is segmented into discrete entities with trigger points, allowing selective rendering of only those entities that are relevant to current user interactions. This segmentation enables the system to load and render entities on-demand rather than pre-loading the entire environment.
2Productivity
If trigger points are used to request rendering operations, then entities can be rendered on-demand, but the timing may be unplanned and still adversely affect performance
Solution Approach 1:
The system performs preliminary analysis of user interaction patterns and pre-determines optimal rendering timings for entities. By anticipating which entities will be interacted with and when, the system can proactively prepare and render entities before they are actually needed, ensuring smooth performance without abrupt rendering operations.
Solution Approach 2:
The system incorporates feedback loops that monitor user interactions and adjust rendering operations in real-time. By continuously analyzing interaction data and feedback, the system can dynamically optimize rendering timing and resource allocation, maintaining consistent performance across different user scenarios.
3Illumination intensity
If the entire environment is rendered at high detail, then visual quality is improved, but computing resources and bandwidth are excessively consumed
Solution Approach 1:
The system applies local quality by rendering entities at different levels of detail based on their relevance to current user interactions. Entities that are actively interacted with or near the user are rendered at high detail, while distant or non-interacted entities are rendered at lower detail or not rendered at all. This selective quality approach maintains visual quality where needed while significantly reducing overall computing resources and bandwidth consumption.
4Ease of operation
If entities are rendered in high detail from the start, then user experience is enhanced, but resource availability is exceeded and latency increases
Solution Approach 1:
The system performs preliminary assessment of user interaction patterns and pre-preps entities that are likely to be interacted with. By anticipating user actions and pre-rendering only those entities, the system can provide high-quality visual experience for relevant entities while maintaining fast session startup and responsiveness for less critical entities.
Solution Approach 2:
The system applies partial action by rendering only the necessary subset of entities at high detail based on predicted user interactions. Rather than rendering all entities at maximum quality, the system renders sufficient detail for the specific interaction context, achieving good user experience while staying within resource constraints and maintaining speed.
Data Source
AI summary
Concepts and technologies are disclosed herein for optimization of resource usage in cross-reality sessions. A computer can receive a request for a cross-reality session, determine entities to be included in the cross-reality session, and optimize resource usage during the cross-reality session. Optimized cross-reality session data can be provided to a device to generate a cross-reality environment that can include the one entity of the entities rendered in the first level of detail. Attention data that can describe interactions in the cross-reality environment can be obtained. The resource usage can be re-optimized based on the attention data. Re-optimizing the resource usage can include determining that the one entity of the entities is to be rendered in a second level of detail that is greater than the first level of detail. An update can be delivered to the device, which can use the update to update the cross-reality environment.