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Error-Resilient Live Video Streaming Using Hybrid Multiple Description Coding and Adaptive Weighted Yamanaka CFA

International Journal of Electronics and Communication Engineering
© 2025 by SSRG - IJECE Journal
Volume 12 Issue 11
Year of Publication : 2025
Authors : D. H. Kitty Smailin, Y.Jacob Vetha Raj
10.14445/23488549/IJECE-V12I11P107
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How to Cite?

D. H. Kitty Smailin, Y.Jacob Vetha Raj, "Error-Resilient Live Video Streaming Using Hybrid Multiple Description Coding and Adaptive Weighted Yamanaka CFA," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 11, pp. 86-102, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I11P107

Abstract:

Live medical video streaming is essential for accurate diagnosis and clinical decision-making in real-time healthcare applications. However, packet loss, unstable network conditions, and limited bandwidth degrade video quality during wireless transmission. The research problem focuses on reconstructing diagnostically important video frames with high visual clarity under lossy channel conditions. Most existing methods use static redundancy, non-adaptive filtering, or high-complexity encoding, which reduce their reliability in time-sensitive medical environments. This research proposes a hybrid solution combining Hybrid Multiple Description Coding (HMDC) and Adaptive Weighted Yamanaka Color Filter Array (AW-YCFA) to address these challenges. HMDC encodes each video frame into two descriptions and one correction stream. The redundancy is calculated dynamically using frame importance and packet loss probability. This ensures critical frames receive stronger protection and bandwidth is used efficiently. The decoding module includes adaptive concealment and interpolation techniques that reconstruct lost data using spatial and temporal references. AW-YCFA improves visual quality by modifying the color filter layout and applying local weights to refine color channel reconstruction. This step reduces chromatic noise and enhances edge sharpness. The system is evaluated using the Kvasir-Capsule-SEG dataset, which contains real-world capsule endoscopy video sequences. Performance is tested under packet loss rates of 10%, 20%, and 30%. The proposed method achieves a 40.91 dB PSNR and 0.9124 SSIM at a 10% loss, and maintains a 35.62 dB PSNR and 0.9467 SSIM at a 30% loss. Compared to existing methods such as ROI-aware video coding, EDBTC, convolutional coding, ARV, end-to-end distortion modeling, BBAG with HTTP/2, scalable video coding with reversible data embedding, and point cloud-based streaming, the proposed system consistently shows higher reconstruction quality and lower delay. It also achieves a compression ratio of 16.4 and 46 FPS throughput with 184 MB memory usage. These results confirm that the system supports high-speed and reliable transmission with accurate visual reconstruction in critical medical video applications.

Keywords:

Hybrid Multiple Description Coding, Adaptive Weighted Yamanaka CFA, Medical Video Transmission, Packet Loss Recovery, Video Frame Reconstruction.

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