INTELLIGENT VERSATILE VIDEO CODING VIDEO DELIVERY OVER 5G: MASSIVE MULTIPLE-INPUT, MULTIPLE-OUTPUT BEAMFORMING MEETS AI-DRIVEN RESOURCE ALLOCATION FOR OPTIMAL SPECTRAL EFFICIENCY
Bibliographic record
Abstract
The increasing demand for high-definition video streaming in 5G networks requires efficient spectrum utilization and adaptive resource management. This paper presents a novel approach, integrating massive multiple-input, multiple-output beamforming and AI-driven resource allocation to enhance the spectral efficiency of versatile video coding-encoded video transmission over 5G networks. The proposed framework employs spatial multiplexing, adaptive beamforming, and deep learning-based dynamic scheduling to optimize bandwidth allocation and mitigate interference. Experimental results demonstrate that the proposed system achieves a 60% increase in spectral efficiency, a 5 dB improvement in peak signal-to-noise ratio, an 8% enhancement in structural similarity index, and a 52% reduction in latency compared to traditional high-efficiency video coding-based 5G transmission methods. Furthermore, throughput improvements of up to 65% were observed in high signal-to-noise ratio scenarios. These results confirm that the system significantly enhances video streaming quality and efficiency, making it highly suitable for real-time applications such as telemedicine, ultra-high-definition video streaming, and smart surveillance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".