Edge Server Frame Prediction for Low-Latency XR over EPON
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Solution Overview
Problem
Current technologies fail to support Extended Reality (XR) services over Ethernet Passive Optical Network (EPON) due to stringent latency requirements, particularly in applications like cloud gaming, which demand low latency and high data rates, and there is no existing protocol for XR service support via optical backhaul.
Innovation Solution
A system and method that utilize an edge server with a play-off buffer, AI-based frame predictor, and SDN controller to manage XR data scheduling, ensuring constant queuing and prediction delays, thereby reconstructing inter-arrival patterns and reducing prediction errors, while implementing application-layer aware MAC scheduling and resource sharing across users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If XR data is transmitted over EPON with strict latency requirements, then low latency is achieved, but network bandwidth and processing speed requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by predicting future XR frames at the edge server before they are actually generated by the XR device. This allows the network to prepare and allocate resources in advance, reducing the actual transmission latency and bandwidth requirements during real-time operation.
Solution Approach 2:
The patent implements dynamic bandwidth allocation and scheduling based on predicted XR traffic patterns. The edge server dynamically adjusts network resource allocation according to the predicted future frames, allowing the system to adapt bandwidth usage to actual needs rather than allocating maximum bandwidth continuously.
2Speed
If edge server processing speed is increased to meet latency requirements, then latency is reduced, but system cost increases
Solution Approach 1:
The edge server performs frame prediction in advance, allowing processing to be distributed over time rather than requiring all processing to occur at the last moment. This temporal distribution reduces peak processing speed requirements and allows for more cost-effective hardware configurations.
Solution Approach 2:
The patent introduces an AI-based frame prediction mechanism as an intermediary between the XR device and the edge server processing. This intermediary handles some of the processing burden by predicting future frames, reducing the actual processing load on the edge server and allowing for more affordable system configurations.
3Device complexity
If multiple users share edge server resources to reduce cost, then cost is reduced, but latency performance deteriorates
Solution Approach 1:
By predicting future frames in advance, the system allows multiple users' data to be processed in a coordinated manner. The prediction mechanism enables the edge server to batch process multiple users' predicted frames efficiently, maintaining low latency even when serving multiple users through resource sharing.
Solution Approach 2:
The system dynamically allocates edge server resources among multiple users based on their predicted traffic patterns and latency requirements. This dynamic resource allocation allows cost-effective multi-user support while maintaining performance guarantees for each user through intelligent scheduling and resource management.
4Measurement precision
If AI-based frame prediction is implemented, then prediction accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent introduces an AI-based frame prediction intermediary that operates at the edge server rather than requiring complex processing at the XR device or in the network core. This placement optimizes the balance between prediction accuracy and processing complexity by leveraging the edge server's computational resources and proximity to both the device and network.
Solution Approach 2:
The frame prediction is performed in advance as a preliminary action, allowing the AI model to process data without time pressure. This temporal headroom enables the use of more accurate but computationally intensive AI algorithms without compromising real-time performance, as the prediction occurs before the actual frame transmission deadline.
Data Source
AI summary
The present invention discloses a network system for supporting XR devices/users over an EPON comprising one or more user XR devices, an edge server and a EPON based connection between said XR devices and said edge server to offer XR services with strict latency bounds over said EPON based connection involving offloading of XR device generated data to the edge server for processing, whereby the edge server process the XR data based on predicted future XR frames of said data after reconstructing EPON dependent inter-arrival pattern of the XR frames.