Hybrid Edge-Cloud Compression for 3D Volumetric 5G Transmission
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Solution Overview
Problem
5G networks face challenges in handling large amounts of 3D data due to bandwidth limitations, leading to inefficiencies in data transmission and processing, particularly in volumetric studios where data is sent for remote processing without effective local and remote processing distribution.
Innovation Solution
A hybrid implementation that dynamically directs the processing of volumetric 3D data between local and remote devices based on available bandwidth, computing power, and load information, using pipelining and artificial intelligence to determine when to send data for remote processing, enabling real-time compression and efficient data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If volumetric 3D data is transmitted over 5G network for remote processing, then processing capability is improved, but bandwidth consumption increases significantly
Solution Approach 1:
The patent segments the volumetric 3D data processing into multiple stages: local preprocessing (feature extraction, initial compression) and remote processing (advanced compression, final rendering). This segmentation allows critical processing to occur locally with minimal bandwidth usage while leveraging remote computing power for tasks that can tolerate transmission delays.
Solution Approach 2:
The patent extracts and processes only the essential features of volumetric 3D data locally before transmission. By taking out and processing key characteristics (depth maps, feature points, mesh structures) at the edge device, the system reduces the quantity of data requiring network transmission while preserving the core information needed for high-quality remote rendering.
2Quantity of substance
If all volumetric 3D data is processed locally, then bandwidth usage is reduced, but processing speed and computational power are limited
Solution Approach 1:
The patent introduces a spatial dimension to processing by distributing computational tasks across two locations (local edge device and remote cloud server). This dimensional expansion allows the system to overcome local processing limitations by leveraging remote computational resources while maintaining bandwidth efficiency through selective data transmission.
Solution Approach 2:
The patent performs preliminary processing actions locally before data transmission, including feature extraction, initial compression, and preprocessing of volumetric data. These preliminary actions reduce the data volume requiring remote processing while ensuring that critical information is preserved, thereby optimizing both bandwidth usage and overall processing speed.
3Quantity of substance
If volumetric 3D data is compressed for transmission, then bandwidth efficiency is improved, but compression complexity and processing time increase
Solution Approach 1:
The patent segments compression into multiple stages: lossless or reversible compression at the local edge device preserving all original data, followed by lossy compression at the remote server that achieves higher compression ratios. This segmented approach distributes computational complexity while achieving overall bandwidth efficiency.
Solution Approach 2:
The patent introduces an intermediary processing stage at the local edge device that performs preliminary compression and feature extraction. This intermediary step simplifies the data before remote transmission, reducing the complexity burden on the remote system while maintaining bandwidth efficiency through progressive compression.
4Productivity
If hybrid edge-cloud processing is implemented, then processing efficiency is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent implements dynamic task distribution between local and remote systems based on real-time conditions such as available bandwidth, local processing capacity, and data characteristics. This dynamic allocation optimizes processing efficiency by adapting to changing system states while managing complexity through flexible, condition-based decision-making.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor system performance metrics (bandwidth availability, processing load, compression ratios) and adjust the distribution of processing tasks accordingly. This feedback loop enables the hybrid system to self-optimize, improving processing efficiency while managing complexity through automated adaptation rather than rigid configuration.
Data Source
AI summary
A hybrid implementation enables sharing the processing of 3D data locally and remotely based on processing and bandwidth factors. The hybrid implementation is flexible in determining what information to process locally, what information to transmit to a remote system, and what information to process remotely. Based on the available bandwidth, computing power/availability locally and computing power/availability remotely, the hybrid implementation is able to direct the processing of the data. By performing some of the processing locally and some of the processing remotely, more efficient processing is able to be implemented.