Adaptive Cross-Layer Wireless Optimization for Distributed XR Tasks
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Solution Overview
Problem
Existing technologies face challenges in efficiently optimizing wireless communications for extended reality (XR) applications, particularly in volatile conditions, due to the decomposition of tasks across different devices and the need for cross-layer optimization.
Innovation Solution
Implementing methods and apparatuses for cross-layer optimization in wireless communications, including determining and adjusting transmission parameters and rates based on triggering conditions, and extending the Session Description Protocol (SDP) for capability negotiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tasks are decomposed across multiple devices for distributed XR applications, then processing capability and flexibility are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the XR application tasks into multiple decomposed tasks that can be executed on different devices (WTRU and Edge devices). Each task is assigned specific parameters and transmission rates, allowing independent optimization while maintaining overall system flexibility. This segmentation resolves the contradiction by enabling distributed execution without requiring complex global coordination.
Solution Approach 2:
The patent implements dynamic parameter adjustment where transmission parameters and rates are determined based on triggering conditions and volatile network conditions. The system continuously adapts parameters for decomposed tasks based on current network state, enabling flexible distributed execution while keeping optimization complexity manageable through localized dynamic adjustments rather than global reconfiguration.
2Productivity
If transmission parameters are dynamically adjusted based on triggering conditions, then communication efficiency in volatile conditions is improved, but processing overhead and latency increase
Solution Approach 1:
The patent establishes triggering conditions that predefine when parameter adjustments should occur. By setting thresholds and conditions in advance, the system avoids continuous parameter recalculation and only adjusts transmission parameters when necessary conditions are met. This preliminary action approach improves transmission efficiency while minimizing processing overhead by avoiding unnecessary calculations during stable network conditions.
Solution Approach 2:
The patent implements a feedback mechanism where transmission parameters are adjusted based on monitored network conditions and triggering events. The system observes network state, compares it against predefined thresholds, and only initiates parameter changes when triggering conditions are satisfied. This feedback-based approach optimizes transmission efficiency while controlling processing overhead by responding only to significant condition changes rather than continuously adjusting parameters.
3Reliability
If cross-layer optimization is implemented for volatile wireless conditions, then reliability of XR applications is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies cross-layer optimization locally to specific decomposed tasks and their associated transmission parameters rather than globally across the entire system. Each task has its own parameter set that can be independently optimized based on local network conditions and task requirements. This local quality approach improves reliability for individual task transmissions while reducing overall system complexity by avoiding global optimization coordination.
Data Source
AI summary
Methods, procedures, and apparatuses for distributed systems cross-layer optimization for extended reality (XR) applications are provided. For example, a method implemented by a wireless transmit/receive unit (WTRU) includes determining a first set of parameters associate with a decomposed task and a first transmission rate for transmitting packets to one or more neighboring nodes; transmitting a first set of packets to the one or more neighboring nodes using the first set of parameters and the first transmission rate; calculating a second transmission rate for transmitting packets to the one or more neighboring nodes; determining a second set of parameters associate with the decomposed task based on a triggering condition being met; and transmitting a second set of packets to the one or more neighboring nodes using the second set of parameters and the second transmission rate.