A Cross-layer Bitrate Optimization Framework for Low-bandwidth Video Transmission Using Lightweight Adaptive Encoding
DOI:
https://doi.org/10.13052/jicts2245-800X.1412Keywords:
Cross-Layer Optimization, Adaptive Encoding, Low-Bandwidth Video Transmission, Control-Theoretic Feedback, Lightweight Encoder EnhancementAbstract
Efficient video transmission over low-bandwidth and unstable networks remains a central challenge for real-time applications such as telemedicine, remote surveillance, and edge-based video analytics. Conventional adaptive streaming approaches such as DASH and HLS operate primarily at the application layer, adjusting bitrates reactively based on buffer occupancy or short-term throughput. These strategies often fail under abrupt bandwidth fluctuations, leading to quality oscillations and excessive rebuffering. This paper proposes a cross-layer bitrate optimization framework that unifies lightweight adaptive encoding with a control-theoretic feedback loop driven by real-time network metrics. The framework jointly considers content complexity, encoder parameters, and network congestion signals to dynamically regulate bitrate across both the network and application layers. A lightweight encoder enhancement module performs perceptually guided bit allocation using saliency-aware analysis, while the control loop ensures fast convergence of target bitrate and stability against throughput variability. Extensive experiments across Wi-Fi, 4G, and simulated edge-network traces show that the proposed system achieves 30–40% bitrate reduction compared with H.264/H.265 adaptive streaming baselines, with PSNR gains up to 1.2 dB and SSIM improvements of 0.02, while reducing buffering time by over 35%. These results establish that the synergy of control-theoretic adaptation and lightweight encoding yields a scalable, low-complexity solution suitable for next-generation low-bitrate video communication systems operating on mobile and edge devices.
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