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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".