XR Point Cloud Scheduling Across MEC Servers to Cut Latency
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Solution Overview
Problem
Holographic communication in extended reality applications faces significant latency issues due to high data rates and computational demands, leading to degraded quality and an unsettling user experience known as the 'uncanny valley effect.
Innovation Solution
Implementing Mobile Edge Computing (MEC) servers with a cloud scheduler to dynamically allocate computation tasks based on network conditions and MEC server capabilities, minimizing latency through efficient job scheduling and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If holographic communication is implemented with high data rates and computational demands, then the quality and realism of XR content is improved, but latency increases causing degraded user experience and uncanny valley effect
Solution Approach 1:
The patent segments the computation tasks by dividing the point cloud data into multiple subsets and distributing them to different MEC servers for parallel processing. This segmentation allows simultaneous computation of different holographic components, reducing overall processing time and latency while maintaining high content quality.
Solution Approach 2:
The patent introduces MEC servers as intermediary nodes between the user equipment and the cloud scheduler. These intermediaries perform computation tasks locally at the network edge, reducing the distance data must travel and minimizing latency while still providing high-quality holographic rendering.
2Loss of time
If Mobile Edge Computing servers are used to reduce latency, then communication latency is minimized, but specialized operations for job scheduling and resource allocation become required
Solution Approach 1:
The cloud scheduler autonomously performs job scheduling and resource allocation without requiring complex manual intervention. The system self-manages task distribution to MEC servers based on available resources and latency requirements, simplifying the operational complexity while maintaining low latency performance.
Solution Approach 2:
The system performs preliminary resource allocation and task scheduling before actual computation begins. The cloud scheduler pre-distributes point cloud subsets to appropriate MEC servers based on predicted performance metrics, ensuring optimal resource utilization and minimizing scheduling complexity during execution.
3Loss of time
If point cloud data is split into multiple subsets and allocated to multiple MEC servers, then processing latency is reduced through parallel computation, but the number of data splits and coordination operations increases
Solution Approach 1:
The patent dynamically adjusts the number and size of data splits based on real-time network conditions and MEC server capabilities. The system adapts the partitioning strategy to balance processing speed with coordination overhead, optimizing the trade-off between parallel processing benefits and operational complexity.
Data Source
AI summary
A method performed by an electronic device of a wireless communication system, includes: receiving, from a first user, points of a cloud related to an application; receiving, from a plurality of mobile edge computing (MEC) servers, a plurality of computing capacities of the plurality of MEC servers; estimating a total latency of the wireless communication system, based on the points of the cloud and the plurality of computing capacities; performing a first operation of minimizing a maximum latency among a plurality of users comprising the first user, based on the total latency; performing a second operation of minimizing a number of splits of the points, based on the minimized maximum latency; performing a splitting of the points to a plurality of subsets and allocating the plurality of subsets to the plurality of MEC servers, respectively.