Digital Twin Synthesis for Video Continuity Under Network Instability
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Solution Overview
Problem
Existing video streaming technologies fail to maintain high-quality, uninterrupted communications during network instability, relying on ineffective strategies like buffering and bandwidth restriction, which lead to corrupted and frozen video streams and dropped connections.
Innovation Solution
Implementing digital twin synthesis, where AI analyzes high-quality images of a speaker to generate 100% synthetic video streams with up to 95% data reduction, automatically switching to synthesized streams during network disruptions, ensuring seamless and high-fidelity video delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If buffering and bandwidth restriction are used to mitigate network instability, then video streaming can continue during mild disruptions, but video quality degrades and connections drop during severe network disturbances
Solution Approach 1:
The patent creates a digital twin (copy) of the speaker using AI synthesis based on captured images and audio. This synthetic video stream serves as a backup that can be deployed when the original video stream degrades due to network issues, maintaining both continuity and quality simultaneously
Solution Approach 2:
The system captures and processes high-quality images of the speaker in advance to create the digital twin model. This preliminary preparation allows the synthetic video stream to be generated instantly when network disruptions occur, avoiding quality degradation during the transition
2Loss of energy
If traditional video codecs are used to reduce bandwidth consumption, then data transmission requirements are lowered, but video quality and stability deteriorate during network congestion
Solution Approach 1:
The digital twin synthesis creates a complete video stream copy that requires minimal bandwidth for transmission since it synthesizes video frames from captured images and audio, rather than transmitting compressed video data. This eliminates the trade-off between bandwidth consumption and stream stability
Solution Approach 2:
The system changes the fundamental parameter of video representation from compressed video frames to synthesized image transformations. This parameter change allows for extremely low bandwidth consumption while maintaining high video quality and stability during network congestion
3Loss of energy
If video resolution and quality are reduced to cope with network congestion, then bandwidth consumption decreases, but information fidelity and communication effectiveness are lost
Solution Approach 1:
The digital twin creates a faithful copy of the speaker's visual appearance and movements, preserving all communication information including facial expressions and gestures, while requiring minimal bandwidth. This eliminates the need to trade information fidelity for bandwidth reduction
Data Source
AI summary
At least one high-quality image of a speaker is captured. A low network quality condition may be detected between a client device and a video service node. In response to detecting the low network quality condition, a data stream comprising changes to the high-quality image of the speaker needed to recreate a representation of the speaker is generated. Transmission of the video stream of the speaker between the client device of the speaker and the video service node is stopped and, simultaneously, transmission of the data stream is begun. A digital twin of the speaker is then generated for display at the client device based on the data stream and the high-quality image of the speaker.