ML-driven Co-optimization of Lightweight Compression and Adaptive Bitrate Allocation for Edge IoT Distributed Video Coding
DOI:
https://doi.org/10.13052/jicts2245-800X.1326Keywords:
Edge IoT, Distributed Video Coding (DVC), Machine Learning, Lightweight Compression, Adaptive Bitrate Allocation, Co-Optimization, Resource-Constrained Networks, 6G CommunicationsAbstract
The increasing demand for real-time video services characterizes next-generation wireless networks. This demand exacerbates the conflict between bandwidth-intensive applications and resource-constrained edge infrastructure. This study proposes an ML-driven co-optimization framework that integrates lightweight compression with adaptive bitrate allocation using distributed edge intelligence. The methodology employs a depthwise separable CNN encoder enhanced by channel pruning and quantization-aware training to minimize computational requirements, achieving model sizes of ≤500 KB and computational complexity of 0.8 GFLOPs per frame on resource-limited nodes. Concurrently, a proximal policy optimization controller is adopted to dynamically adjust bitrate based on real-time channel state information and motion complexity features. A federated alternating optimization mechanism jointly reduces latency, energy consumption, and distortion while preserving data privacy. Experimental validation on edge IoT testbeds demonstrated substantial improvements over state-of-the-art baselines, achieving 42.7% lower encoding latency, 3.2 dB higher PSNR, and 38.5% reduced energy consumption with sub-100 ms processing times. By addressing the fundamental disconnect between compression and transmission optimization, this framework provides a scalable solution for 6G-enabled massive IoT video systems. It effectively bridges theoretical machine learning advances with practical deployment constraints in ultra-reliable low-latency communication environments.
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Arslan Shafique, Abid Mehmood, Moatsum Alawida, Abdul Nasir Khan, Enhancing privacy in data transmission between IoT devices: A robust encryption and embedding framework for secure and meaningful image communication, Journal of Information Security and Applications, Volume 93, 2025, 104112. https://doi.org/10.1016/j.jisa.2025.104112.
Mehdi Hosseinzadeh, Jawad Tanveer, Saqib Ali, Marcia L. Baptista, Farhad Soleimanian Gharehchopogh, Shakia Rajabi, Thantrira Porntaveetus, Sang-Woong Lee, An energy-focused model for batteryless IoT: Vortex wireless power transfer and fog computing in 6 G networks, Internet of Things, Volume 32, 2025, 101657. https://doi.org/10.1016/j.iot.2025.101657.
Minallah, Nasru, Gul, Saman and Bokhari, M.M.. (2015). Performance Analysis of H.265/HEVC (High-Efficiency Video Coding) with Reference to Other Codecs. 216–221. 10.1109/FIT.2015.46.
Lonkar, Mr and Barwat, Mr. (2019). Performance Parameters of Hevc Video Codec. International Journal of Engineering and Advanced Technology. 8. 365–369. 10.35940/ijeat.E7711.088619.
Huangyuan, Qingxiong, Song, Li, Luo, Zhengyi, Wang, Xiangwen and Zhao, Yanan. (2015). Performance evaluation of H.265/MPEG-HEVC encoders for 4K video sequences. 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014. 10.1109/APSIPA.2014.7041782.
Sang-Uk Park, Young-Yoon Lee, Chang-Su Kim, Sang-Uk Lee, CDV-DVC: Transform-domain distributed video coding with multiple channel division, Journal of Visual Communication and Image Representation, Volume 24, Issue 5, 2013, Pages 534–543. https://doi.org/10.1016/j.jvcir.2013.03.016.
Hu, Chunyun, Zhao, Yafan, Yu, Long, Jiang, Yang and Xiong, Yunhui. (2020). A simple encoder scheme for distributed residual video coding. Multimedia Tools and Applications. 79. 10.1007/s11042-020-08811-y.
Khursheed, Shahzad, Badruddin, Nasreen, Jeoti, Varun, Vukobratovic, Dejan and Hashmani, Manzoor. (2023). Low Computational Coding-Efficient Distributed Video Coding: Adding a Decision Mode to Limit Channel Coding Load. Entropy. 25. 241. 10.3390/e25020241.
Wu, W., Fouzi, H., Benamar, B. et al. Deep learning-based stacked models for cyber-attack detection in industrial internet of things. Neural Comput & Applic (2025). https://doi.org/10.1007/s00521-025-11418-9.
Younas, T., Xing, X., Shen, J. et al. 3D massive MIMO with massive connectivity for internet of things devices. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03477-4.
Liu, Tong and Qin, Feng. (2025). Study on Industrial Wastewater Pollution Monitoring Technology Based on NB-IoT Wireless Communication Technology. International Journal of Grid and High Performance Computing. 17. 1–20. 10.4018/IJGHPC.374211.
Jiaqin Wang, Kai Liu, Hantao Li, LSTM-based graph attention network for vehicle trajectory prediction, Computer Networks, Volume 248, 2024, 110477. https://doi.org/10.1016/j.comnet.2024.110477.
Ang Ji, Zhuo Liu, Lingyun Su, Zhe Dai, A hybrid framework for spatio-temporal traffic flow prediction with multi-scale feature extraction, Information Sciences, Volume 716, 2025, 122259. https://doi.org/10.1016/j.ins.2025.122259.
Xu, T., Zhao, W. and Duan, Z. BDFGNet: A Lightweight Salient Object Detection Network Based on Background Denoising and Feature Generation. Arab J Sci Eng 49, 4365–4381 (2024). https://doi.org/10.1007/s13369-023-08484-3.
Yang, Zi, Choudhary, Samridhi, Kunzmann, Siegfried and Zhang, Zheng. (2023). Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding. 10.48550/arXiv.2306.01076.
Bendelhoum, Mohammed, Ridha Ilyas, Bendjillali, Miloud, Kamline and Tadjeddine, Abderrazak. (2025). Enhancing facial expression recognition using coordinate attention mechanism and MobileNetV3. Multimedia Tools and Applications. 1–34. 10.1007/s11042-025-21059-8.
Li, Haibo, Cheng, Yong, Zhang, Qian and Chen, Lingkun. (2025). DSS-MobileNetV3: An Efficient Dynamic-State-Space- Enhanced Network for Concrete Crack Segmentation. Buildings. 15. 1905. 10.3390/buildings15111905.
Ismail, A.A., Khalifa, N.E. & El-Khoribi, R.A. A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study. Cluster Comput 28, 184 (2025). https://doi.org/10.1007/s10586-024-04893-7.
Ahammad, I. Fog Computing Complete Review: Concepts, Trends, Architectures, Technologies, Simulators, Security Issues, Applications, and Open Research Fields. SN COMPUT. SCI. 4, 765 (2023). https://doi.org/10.1007/s42979-023-02235-9.
Xu, Z., Jiang, L. Federated learning-based fault location and identification in hybrid AC/DC distribution systems considering bidirectional power flow. J. Eng. Appl. Sci. 72, 133 (2025). https://doi.org/10.1186/s44147-025-00694-w.
Rathee, A., Dalal, S. A systematic literature review of machine learning-based resource allocation techniques in cloud computing. Computing 107, 179 (2025). https://doi.org/10.1007/s00607-025-01526-8.
Liu, J., Zhang, B., Cao, X. (2025). ROI-Aware Dynamic Network Quantization for Neural Video Compression. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15305. Springer, Cham. https://doi.org/10.1007/978-3-031-78169-8_22.
Li Xu, Chang Wu, Linyi Huang, Shengyu Wei, Gang He, Yunsong Li, Xinquan Lai, Towards coding for VoD application: An enhanced video compression system with a content-fitted recursive restoration network, Digital Signal Processing, Volume 122, 2022, 103368. https://doi.org/10.1016/j.dsp.2021.103368.
Tamal Pal, Sipra Das Bit, Low overhead spatiotemporal video compression over smartphone based Delay Tolerant Network, Journal of Visual Communication and Image Representation, Volume 70, 2020, 102813. https://doi.org/10.1016/j.jvcir.2020.102813.
Hayashi, Masahito & Wang, Kun. (2022). Dense Coding with Locality Restriction on Decoders: Quantum Encoders versus Superquantum Encoders. PRX Quantum. 3. 10.1103/PRXQuantum.3.030346.
Fu, Zihao, Lam, Wai, Yu, Qian, So, Anthony, Hu, Shengding, Liu, Zhiyuan and Collier, Nigel. (2023). Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder. 10.48550/arXiv.2304.04052.
Eshwarappa, N.M., Baghban, H., Hsu, CH. et al. Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing. J Cloud Comp 14, 44 (2025). https://doi.org/10.1186/s13677-025-00734-z.
Yuvarani, R., Mahaveerakannan, R., Thanarajan, T. et al. Energy-aware cluster head optimization and secure blockchain integration for heterogeneous 6G-enabled IoMT networks. Sci Rep 15, 30009 (2025). https://doi.org/10.1038/s41598-025-15462-2.
Deligiannis, Nikos, Verbist, Frederik, Slowack, Jurgen, Van de Walle, Rik, Schelkens, Peter, Munteanu, Adrian. (2014). Progressively Refined Wyner-Ziv Video Coding for Visual Sensors. Acm Transactions on Sensor Networks. 10. 10.1145/2530279.
Krishnaraj, N., Bellam, K., Sivakumar, B., Daniel, A. (2022). The Future of Cloud Computing: Blockchain-Based Decentralized Cloud/Fog Solutions – Challenges, Opportunities, and Standards. In: Baalamurugan, K., Kumar, S.R., Kumar, A., Kumar, V., Padmanaban, S. (eds) Blockchain Security in Cloud Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-70501-5_10.
Liu, Peng & Cao, Xiaofan & Jia, Yujiao. (2024). Performance evaluation and analysis of scalable Raspberry Pi 4 Model B clusters. 10.21203/rs.3.rs-4460804/v1.
Cheng, Z., Liu, J., Zhang, J. (2022). An Improved Mobilenetv2 for Rust Detection of Angle Steel Tower Bolts Based on Small Sample Transfer Learning. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science, vol. 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_13.
Yasser, I., Abd El-Khalek, A.A., Twakol, A., Abo-Elsoud, ME., Salama, A.A., Khalifa, F. (2022). A Hybrid Automated Intelligent COVID-19 Classification System Based on Neutrosophic Logic and Machine Learning Techniques Using Chest X-Ray Images. In: Hassanien, AE., Elghamrawy, S.M., Zelinka, I. (eds) Advances in Data Science and Intelligent Data Communication Technologies for COVID-19. Studies in Systems, Decision and Control, vol. 378. Springer, Cham. https://doi.org/10.1007/978-3-030-77302-1_7.
Shi, Guo-Xing, Wang, Yi-Na, Yang, Zhen-Fa, Guo, Ying-Qing and Zhang, Zhi-Wei. (2024). Wildfire Identification Based on an Improved MobileNetV3-Small Model. Forests. 15. 1975. 10.3390/f15111975.
Ma, Zhichao, Luo, Yutong, Zhang, Zheyu, Sun, Aijia, Yang, Yinuo and Liu, Hao. (2025). Reinforcement Learning Approach for Highway Lane-Changing: PPO-Based Strategy Design. 10.20944/preprints202506.2087.v1.
Li, H., Luo, K., Zeng, B. et al. GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning. Int J Comput Vis 132, 2331–2349 (2024). https://doi.org/10.1007/s11263-023-01978-5.
Ben Hazem, Zied, Saidi, Firas, Guler, Nivine and Altaif, Ali. (2025). Reinforcement learning-based intelligent trajectory tracking for a 5-DOF Mitsubishi robotic arm: comparative evaluation of DDPG, LC-DDPG, and TD3-ADX. International Journal of Intelligent Robotics and Applications. 1–21. 10.1007/s41315-025-00475-x.
Martínez-Rach, Miguel, López Granado, Otoniel, Pi nol, Pablo, Malumbres, Manuel, Oliver, José and Calafate, Carlos. (2007). Quality assessment metrics vs. PSNR under packet loss scenarios in MANET wireless networks. 31–36. 10.1145/1290050.1290058.
Imran, Noreen, Seet, Boon-Chong and Fong, Alvis. (2015). Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures. SpringerPlus. 4. 513. 10.1186/s40064-015-1300-4.
Mao, Zhou, Shuai, Tong, Liang, Shuo, Zhang, Liguo and Li, Sizhao. (2022). Research on Side-Channel Attack Method Based on LSTM. 10.1007/978-3-031-06767-9_52.
Li, Haibo, Cheng, Yong, Zhang, Qian and Chen, Lingkun. (2025). DSS-MobileNetV3: An Efficient Dynamic-State-Space- Enhanced Network for Concrete Crack Segmentation. Buildings. 15. 1905. 10.3390/buildings15111905.
Duan, Juzheng, Zhang, Min, Wang, Jing, Han, Sang, Chen, Xun and Yang, Xiaolong. (2020). VCC-DASH: A Video Content Complexity-Aware DASH Bitrate Adaptation Strategy. Electronics. 9. 230. 10.3390/electronics9020230.
Li, Yongfei, Guo, Yuanbo, Fang, Chen, Wang, Yifeng, Chen, Qingli and Hu, Yongjin. (2025). A distributed bijection-backdoor-based adversarial examples defense method in federated learning. International Journal of Machine Learning and Cybernetics. 1–17. 10.1007/s13042-025-02750-6.
Forch, Valentin, Franke, Thomas, Rauh, Nadine and Krems, Josef. (2017). Are 100 ms Fast Enough? Characterizing Latency Perception Thresholds in Mouse-Based Interaction. 45–56. 10.1007/978-3-319-58475-1_4.
