Intelligent multimedia content delivery in 5G/6G networks: A reinforcement learning approach

• Published in: Transactions on emerging telecommunications technologies Vol. 35; no. 4
• Main Authors: Iqbal, Muhammad Jamshaid, Farhan, Muhammad, Ullah, Farhan, Srivastava, Gautam, Jabbar, Sohail
• Format: Journal Article
• Language: English
• Published: 01.04.2024
• ISSN: 2161-3915; 2161-3915
• DOI: 10.1002/ett.4842

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. This study proposes reinforcement learning (RL)‐based intelligent multimedia content distribution. RL algorithms learn from the network environment and generate optimum judgments that incorporate several aspects of the suggested framework.