{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:08:55Z","timestamp":1782313735737,"version":"3.54.5"},"reference-count":0,"publisher":"River Publishers","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JICTS"],"abstract":"<jats:p>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\u201340% 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.<\/jats:p>","DOI":"10.13052\/jicts2245-800x.1412","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:31:57Z","timestamp":1773714717000},"source":"Crossref","is-referenced-by-count":1,"title":["A Cross-layer Bitrate Optimization Framework for Low-bandwidth Video Transmission Using Lightweight Adaptive Encoding"],"prefix":"10.13052","author":[{"given":"Yusen","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5195","published-online":{"date-parts":[[2026,3,15]]},"container-title":["Journal of ICT Standardization"],"original-title":[],"link":[{"URL":"https:\/\/journals.riverpublishers.com\/index.php\/JICTS\/article\/download\/31293\/23611","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.riverpublishers.com\/index.php\/JICTS\/article\/download\/31293\/23613","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.riverpublishers.com\/index.php\/JICTS\/article\/download\/31293\/23611","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:31:52Z","timestamp":1773801112000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.riverpublishers.com\/index.php\/JICTS\/article\/view\/31293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.13052\/jicts2245-800x.1412","relation":{},"ISSN":["2246-0853","2245-800X"],"issn-type":[{"value":"2246-0853","type":"electronic"},{"value":"2245-800X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,15]]}}}