## 3 System Design
### Dynamic Load Balancing and Fault Prediction  
Based on historical performance data and real-time monitoring metrics, the system implements a predictive load balancing mechanism using a hybrid Multi-layer Perceptron (MLP) with Long Short-Term Memory (LSTM) architecture. LSTMs are specialized recurrent neural networks designed to capture long-term dependencies in sequential data through a sophisticated gating mechanism that selectively remembers or forgets information [59]. This capability is crucial for analyzing temporal patterns in network performance metrics, where historical trends strongly influence future behavior. MLPs, in contrast, are traditional feedforward neural networks that excel at non-linear classification and regression tasks [60]. By introducing LSTM layers before the MLP classifier, our system gains the ability to detect subtle temporal anomalies in resource utilization patterns--such as gradually increasing memory consumption or cyclical network congestion--that simple threshold-based or non-recurrent models would miss. This hybrid architecture effectively addresses the temporal nature of network failures, which typically manifest as evolving patterns rather than instantaneous events, perfectly aligning with the system's need for proactive fault detection and recovery.  
The system collects data every 50 ms, incorporating the previous failure probability and current resource utilization metrics to build a comprehensive dataset for model training and online adaptation:  
\[P_{failure}(t)=\text{MLP}(\text{LSTM}([P_{failure}(t-1),CPU(t),Mem(t),Net(t)])) \tag{11}\]  
where the LSTM component captures temporal dependencies in the sequence \([P_{failure}(t-1)\), \(CPU(t),Mem(t),Net(t)]\), and the MLP performs final classification. The model employs transfer learning based on the assumption that resource-failure relationships remain consistent across similar devices in short time periods, enabling effective knowledge transfer across the deployment fleet. Online learning continuously adjusts model parameters through comparative learning against pre-trained baselines, with the update rule:  
\[\theta_{t+1}=\theta_{t}-\mathbf{\alpha}\
abla L(\text{ground truth},P_{failure}(t))- \mathbf{\beta}\
abla L_{transfer}(\theta_{pre},\theta_{t}) \tag{12}\]  
where \(\mathbf{\alpha}\) and \(\mathbf{\beta}\) control the online learning rate and transfer learning regularization, respectively. When the predicted probability exceeds a predefined threshold, the system proactively triggers link reconstruction to ensure service continuity.  
Thus, at the protocol design level, our system fully leverages the extremely low transmission latency advantages of the UDP protocol, combined with the reliability, security, and intelligent flow control capabilities provided by the SRT protocol. This synergy achieves efficient and stable multi-hop chain-based real-time stream broadcasting in complex public network environments, effectively supporting dynamic chain topology reconstruction and self-healing from link breaks. This provides a robust transmission protocol foundation for large-scale, low-cost, real-time, multi-venue video systems.