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6.3 Edge‑Aware Transport Synergy

By exposing transport statistics to the RL agent, JN‑01 aligns encoding aggressiveness with network congestion, avoiding buffer under‑runs and re‑buffering events typical in conventional pipelines. jawanikanukshas01part2720phevcwebdlhi hot

• Latency: Across all scenarios, JN‑01 reduced end‑to‑end latency by an average of 37 % (from 92 ms to 58 ms).
• Quality: VMAF improvement ranged from +4.6 to +5.3 points, confirming that bitrate savings did not compromise perceptual quality.
• Resource Use: Average GPU utilization 68 %; CPU 45 %—well within edge‑node capacity.
• Scalability: The system sustained 10 000 concurrent viewers with < 150 ms latency on the 5 G edge‑cloud testbed, a 2.8× improvement over the baseline.

• Dr. Aria S. Patel, Department of Computer Science, Global Institute of Technology
• Prof. Ming‑Wei Liu, School of Electrical Engineering, Pacific University
• Dr. Elena V. García, Centre for Media Systems, Universidad del Sol
• Dr. Ravi K. Jawanika, Institute for Advanced Telecommunications, New Delhi