Intelligent multimedia content delivery in 5G/6G networks: A reinforcement learning approach
Bibliographic record
Abstract
Abstract Multimedia content in 5G/6G networks makes safe, confidential, and efficient content delivery difficult. Intelligent systems that adapt to the ever‐changing network environment are needed to distribute multimedia content in these networks. Reinforcement learning (RL) can optimize multimedia content distribution based on network congestion, capacity, and user preferences. This study proposes RL‐based intelligent multimedia content distribution. RL algorithms learn from the network environment and generate optimum judgments incorporating several aspects of the suggested framework. The framework delivers multimedia material securely and privately with great quality. This study provides an intelligent multimedia content delivery architecture that uses RL approaches to solve 5G/6G content delivery problems. This research presents an RL system optimized with the double DQN algorithm having a reward of 51604.93 in 7000 episodes for efficient video file sharing on intracity buses. The RL agent balances network congestion and bandwidth by leveraging multiple sources such as bus and intersection caches and base stations, improving secure multimedia content delivery in 5G/6G networks and enhancing the passenger experience. The study confirms the system's effectiveness using reward and loss metrics and identifies potential future research directions. Future work could explore additional RL algorithms, scalability for larger networks, complex delivery scenarios, and integration with blockchain and edge computing for improved security and efficiency in multimedia content delivery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".