## 2 Related Work
### AI in Decentralized Video Streaming  
The integration of artificial intelligence (AI) and machine learning (ML) techniques has emerged as a transformative approach for addressing the complex challenges in decentralized video streaming systems [38; 39; 40]. Recent advances in AI-driven video streaming focus on three primary areas: adaptive bitrate control, quality of experience (QoE) optimization, and intelligent resource management. Modern reinforcement learning (RL) approaches have demonstrated significant improvements in adaptive bitrate streaming. [PERSON] et al. [41] proposed an edge-assisted RL framework that leverages distributed computing resources to optimize video delivery decisions in real-time, achieving up to 25% improvement in QoE metrics compared with traditional heuristic methods. Similarly, [PERSON] et al. [42] developed a neural adaptive bitrate algorithm that uses deep Q-networks (DQN) to learn optimal bitrate selection policies from network conditions and user preferences, demonstrating superior performance in dynamic network environments.  
The emergence of federated learning has opened new possibilities for collaborative optimization in distributed streaming systems. [PERSON] et al. [43] introduced FedStream, a federated learning framework that enables edge nodes to collaboratively train adaptive streaming models while preserving privacy. This approach allows the system to benefit from collective intelligence across distributed nodes without centralizing sensitive data. [PERSON] et al. [44] further extended this concept by proposing federated adaptive bitrate streaming that leverages multi-agent reinforcement learning for coordinated decision-making across peer networks. Meta-learning techniques have also shown promise in addressing the heterogeneity and dynamics of decentralized streaming environments. [PERSON] et al. [45] developed a meta-reinforcement learning approach that enables rapid adaptation to new network conditions and user scenarios with minimal training data. This is particularly valuable in P2P systems where nodes frequently join and leave the network, requiring quick adaptation to changing topologies and conditions.  
Quality of experience optimization has been enhanced through AI-driven approaches that consider multiple factors beyond traditional network metrics. [PERSON] et al. [46] proposed a QoE-aware RL algorithm for multi-path video streaming that jointly optimizes bitrate, buffer management, and path selection to maximize user satisfaction. Their approach incorporates perceptual quality models and user behavior patterns to make more informed streaming decisions. Edge intelligence has become increasingly important for real-time video processing and delivery. [PERSON] et al. [47] introduced neural buffer management techniques that use recurrent neural networks to predict optimal buffer sizes and prefetching strategies based on content characteristics and network dynamics. [PERSON] et al. [48] developed intelligent edge caching mechanisms that use deep learning to predict content popularity and optimize cache placement for personalized video streaming. Within the context of our proposed chain-topology system, these AI techniques can be integrated to enhance autonomous operation and performance optimization. For instance, the fault detection mechanism described in our system could be augmented with neural anomaly detection models trained on historical network telemetry data. The adaptive relay path selection could benefit from RL algorithms that learn optimal routing decisions based on real-time network conditions and QoS requirements. Furthermore, federated learning approaches could enable collaborative optimization across the chain topology without requiring centralized coordination, maintaining the decentralized nature of the system while benefiting from collective intelligence [33].