MR Computation Offloading Using Multi-Modal User Input
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Solution Overview
Problem
Existing methods for mixed reality (MR) applications in vehicle systems face challenges with high latency and bandwidth usage due to inefficient computation offloading, which affects user experience and resource utilization.
Innovation Solution
The system employs edge-driven computation offloading based on multi-modal user input, such as eye tracking, head movement, and voice commands, to determine the level of computation offloading to an edge server, thereby optimizing resource utilization and reducing latency and bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If computation is offloaded to edge server, then processing power is improved, but latency increases
Solution Approach 1:
The system dynamically adjusts computation offloading decisions based on real-time user behavior data, network conditions, and task characteristics. The offloading ratio is not fixed but adapts continuously to optimize the balance between processing power and latency, resolving the contradiction by making the system responsive to changing conditions rather than static.
Solution Approach 2:
The system changes key parameters such as offloading ratio, task selection criteria, and resource allocation based on user behavior patterns detected through multi-modal sensors. By adjusting these parameters dynamically, the system optimizes processing power utilization while minimizing latency impact, effectively resolving the contradiction between enhanced processing and time loss.
2Productivity
If computation is offloaded to edge server, then resource utilization is improved, but bandwidth usage increases
Solution Approach 1:
The system applies partial offloading rather than complete offloading, sending only the necessary portion of computational tasks to the edge server based on user behavior analysis. This partial action approach improves resource utilization by leveraging edge computing capabilities while minimizing bandwidth consumption by keeping essential processing local, thus resolving the contradiction between productivity and quantity of data transmitted.
Solution Approach 2:
The system implements different processing strategies for different tasks and users based on local conditions. By analyzing user behavior patterns locally and making decentralized offloading decisions, the system optimizes resource utilization at the edge while reducing unnecessary bandwidth usage, effectively resolving the contradiction between improved productivity and reduced quantity of data transmission.
3Ease of operation
If multi-modal user input is collected, then user experience is improved, but device complexity increases
Solution Approach 1:
The system uses a unified multi-modal input framework that handles various input types (eye tracking, head movement, voice commands) through a common processing architecture. This universal approach improves user experience by supporting diverse interaction methods while avoiding the complexity of separate processing systems for each input modality, effectively resolving the contradiction between ease of operation and device complexity.
Solution Approach 2:
The system automatically processes and interprets multi-modal user inputs without requiring complex manual configuration or intervention. By implementing self-service mechanisms for input processing, pattern recognition, and offloading decision-making, the system enhances user experience through intuitive interaction while minimizing the operational complexity burden on users and the system.
Data Source
AI summary
System and method for reducing latency and bandwidth usage in reality devices comprises a reality device and one or more processors. The reality device operably collects behavior data of a user. The one or more processors operable to determine a level of computation offloading to an edge server based on the behavior data, and offload one or more tasks to the edge server based on the level of computation offloading.