The propagation faults are crippling the real-time streaming of content in learning environments with limited bandwidth, resulting in a small loss of packets that propagate into severe packet synchronization errors. The issue that is dealt with in this research is the preservation of continuity in streams through network conditions with throughput that is less than 1.5 Mbps and jitter that is greater than 150ms. The study refers to the architecture as Cross-Layer Fault Mitigation (CLFM), which combines a predictive packet-recovery algorithm and a content-feedback buffer management system. The approach consisted of modeling a limited educational network with the help of NS-3 and testing the work of the CLFM in comparison with the performance of the conventional WebRTC and HLS applications. It has had to make the integrity of the Reference Frames (I-frames) and high-priority metadata that are required to achieve pedagogical clarity (i.e., slide transitions and audio sync) a priority. The experimental results show that the CLFM framework eliminates propagation-induced frame stalls by 34.2 % as compared to conventional adaptive bitrate (ABR) techniques. In addition, the system was found to have a Peak Signal-to-Noise Ratio (PSNR) of 28.5 dB up to a packet loss rate of 12%, which is an 87% improvement in visual stability compared to baseline protocols. The study finds that fault origins at the transport layer can be corrected via selective forward error correction to provide intelligible geographically underserved instructional content to educational platforms. These results provide a technical solution to scale up the digital divide of remote engineering and technical education.
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