Predictive Virtual World Content Streaming for Limited Storage
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Solution Overview
Problem
Current methods for distributing virtual world content require large storage capacities, making it difficult for devices with limited storage, such as cellular phones and portable gaming systems, to participate in networked virtual worlds, and do not efficiently manage content updates, leading to storage limitations and seamless presentation issues.
Innovation Solution
A predictive content transmission system that uses a server to determine and transmit only the necessary virtual world content based on the client's behavior, position, and subscription level, employing algorithms like Location-Velocity Content Transfer and Storyline Content Transfer to selectively stream content, reducing storage demands and ensuring seamless virtual world experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual world content is distributed via tangible medium or single network transfer, then content can be transmitted to client, but client requires large storage capacity which excludes devices with limited storage
Solution Approach 1:
The virtual world content is segmented into multiple zones or regions that can be independently streamed to the client. Instead of requiring the client to store the entire virtual world, only the relevant segments are transmitted and maintained in a streaming buffer, enabling devices with limited storage to participate.
Solution Approach 2:
The system performs preliminary content selection and buffering based on predicted user behavior and avatar position. Content is pre-loaded into the streaming buffer before the user actually needs it, ensuring seamless experience while minimizing storage requirements on the client device.
2Reliability
If all virtual world content is transmitted to client, then complete virtual world experience is provided, but storage requirements grow continuously and management becomes complex
Solution Approach 1:
The content streaming system is dynamic and adaptive, adjusting what content is transmitted and buffered based on real-time factors such as avatar position, movement velocity, and predicted user behavior. This dynamic approach ensures the virtual world experience remains seamless while automatically managing complexity through algorithmic decision-making.
Solution Approach 2:
The system incorporates feedback mechanisms where client behavior patterns and avatar movement are continuously monitored and fed back into the content selection algorithm. This feedback loop enables the system to learn user preferences and optimize content transmission, maintaining experience quality while simplifying management through automated adaptive behavior.
3Productivity
If content is updated frequently to maintain user interest, then virtual world remains engaging, but update management becomes complex and storage is consumed
Solution Approach 1:
Content updates are performed preliminarily by the server before the client needs them. The server pushes updated content into the streaming buffer in advance, and the client simply retrieves the updated content when needed without managing update processes locally. This shifts update management complexity from the client to the server.
Solution Approach 2:
The server acts as an intermediary that centralizes content update management. Instead of the client managing frequent updates, the server handles content generation, compression, and buffering, then streams only necessary updates to the client. This intermediary approach maintains high update frequency while reducing client-side complexity.
Data Source
AI summary
Virtual world content is transmitted from at least one server to at least one client computer by defining a camera position on the client computer and projecting a geometrical shape from the camera position in the direction of a viewing vector. This geometrical shape defines a subsection of the virtual world content, which is then transmitted to the particular client, where it is maintained within a content cache.