Radio Access Network Configuration for Semantic Video Error Correction
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Solution Overview
Problem
Current communications networks struggle to deliver high-fidelity quality of experience (QoE) for emerging applications like AR/VR/XR and cloud gaming due to varying quantitative constraints, especially in bit-exact transmission requirements that exceed compression gains, making it difficult to optimize transmission strategies for reliable video delivery.
Innovation Solution
Implement a video approximate semantic communications method that includes a smart video decoder with AI/ML-based semantic error correction and hybrid automatic repeat request (HARQ) feedback to mitigate and conceal PHY transport errors, integrating video codec knowledge at the transmitter and receiver for bit-inexact communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bit-exact transmission requirements are enforced to ensure high-fidelity QoE, then transmission reliability is improved, but transmission complexity and resource consumption increase beyond compression gains
Solution Approach 1:
The patent changes the fundamental parameter from bit-exact transmission to bit-inexact transmission with semantic correctness. Instead of requiring every bit to be transmitted perfectly, the system allows bitwise errors as long as the semantic meaning is preserved through AI/ML-based error correction at the receiver end, thereby reducing transmission complexity while maintaining reliability
Solution Approach 2:
The patent substitutes traditional mechanical error correction mechanisms (retransmissions, complex FEC codes) with AI/ML-based semantic error correction. The receiver uses trained neural networks to detect and correct semantic errors in received video data, replacing complex transmission control mechanisms with intelligent post-processing
2Reliability
If traditional FEC decoding is applied to correct bitwise transmission errors, then error correction capability is improved, but residual errors and video artifacts remain that degrade QoE
Solution Approach 1:
The patent combines traditional FEC decoding with AI/ML-based semantic error correction in a composite approach. First, conventional FEC corrects obvious bitwise errors, then the AI/ML model performs semantic error correction on residual errors that FEC cannot handle, achieving both error correction capability and video quality precision
Solution Approach 2:
The patent introduces an intermediary AI/ML-based semantic error correction layer between traditional FEC decoding and final video reconstruction. This intermediary corrects residual errors that FEC misses, acting as a bridge that enhances both error correction capability and video quality without requiring complete retransmission
3Manufacturing precision
If video codec knowledge is integrated at both transmitter and receiver for semantic error correction, then QoE is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training AI/ML models with video codec knowledge before deployment. The models are trained offline on large datasets with codec-specific error patterns, so during actual transmission, the pre-trained models can quickly correct semantic errors without real-time complex processing, improving video quality while managing system complexity
Solution Approach 2:
The receiver performs self-service semantic error correction using locally deployed AI/ML models that have internalized video codec knowledge. Instead of requiring complex server-side processing or frequent retransmissions, the receiver autonomously corrects semantic errors using its pre-trained models, improving video quality while reducing overall system complexity
4Adaptability or versatility
If semantic error correction is enabled to allow bit-inexact transmissions, then transmission scalability is improved, but additional processing requirements increase
Solution Approach 1:
The patent applies partial action by implementing semantic error correction only when needed - specifically when bitwise errors are detected that affect semantic meaning. The system selectively applies AI/ML correction to erroneous regions rather than processing entire video streams uniformly, improving transmission scalability while reducing unnecessary processing energy consumption
Data Source
AI summary
An apparatuses for radio access network configuration for video approximate semantic communications includes a transceiver that receives from a transmitter a bitstream corresponding to a video coded data transmission wherein the received bitstream includes bitwise transmission errors and a processor that performs FEC decoding and correcting at least one bitwise transmission error of the video coded data transmission whereas at least one bitwise transmission error is left in a bit-inexact reception of the video coded data transmissions post FEC decoding, applies, by a smart video decoder in a video approximate semantic communications mode, semantic error correction to decoded video coded data transmissions to correct and conceal one or more video artifacts in response to the bit-inexact reception of the video coded data transmissions post FEC decoding, and reconstructs a video uncoded representation of concealed approximate semantic content relative to the received bitstream corresponding to the video coded data transmission.