TY - GEN
T1 - ACORN
T2 - 19th International Conference on Mobility, Sensing and Networking, MSN 2023
AU - Lei, Jiale
AU - Yang, Peihao
AU - Kong, Linghe
AU - Ma, Yehan
AU - Lu, Xingjian
AU - Lin, Deyu
AU - Chen, Guihai
AU - Zhao, E.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - IoT devices are enabled to capture and upload videos with increasing bitrates. Massive IIoT is eager for effective video processing techniques to satisfy the requirements of real-time video services. With the emergence of 5G-unlicensed (5G-U), ultra-low latency video applications become possible. However, existing encoding standards for video services in Web 2.0, such as H.265, are not naturally designed for IIoT video streaming, leading to bandwidth pressure where 5G-U coexists with various other wireless signals. To tackle this problem and to support low-latency video utilization by IIoT video sources, we propose an Adaptive Compression-Reconstruction framework named ACORN, which is based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. We design a QoE-aware parameter selection mechanism to deal with volatile network environments during compression. With learnable gated convolution layers and channel-wise soft-thresholding operators, ACORN also builds a real-time reconstruction module. Experimental results reveal that video analytics can be conducted on compressed frames. The reconstruction algorithm in ACORN is with 1-4 ~dB improvements. Moreover, both the encoding time cost and the encoded video volume are reduced by more than 4 × under the ACORN framework.
AB - IoT devices are enabled to capture and upload videos with increasing bitrates. Massive IIoT is eager for effective video processing techniques to satisfy the requirements of real-time video services. With the emergence of 5G-unlicensed (5G-U), ultra-low latency video applications become possible. However, existing encoding standards for video services in Web 2.0, such as H.265, are not naturally designed for IIoT video streaming, leading to bandwidth pressure where 5G-U coexists with various other wireless signals. To tackle this problem and to support low-latency video utilization by IIoT video sources, we propose an Adaptive Compression-Reconstruction framework named ACORN, which is based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. We design a QoE-aware parameter selection mechanism to deal with volatile network environments during compression. With learnable gated convolution layers and channel-wise soft-thresholding operators, ACORN also builds a real-time reconstruction module. Experimental results reveal that video analytics can be conducted on compressed frames. The reconstruction algorithm in ACORN is with 1-4 ~dB improvements. Moreover, both the encoding time cost and the encoded video volume are reduced by more than 4 × under the ACORN framework.
KW - compressive imaging reconstruction
KW - industrial 5G-U
KW - industrial Internet of things
KW - realtime streaming
KW - video compression
UR - https://www.scopus.com/pages/publications/85197523356
U2 - 10.1109/MSN60784.2023.00051
DO - 10.1109/MSN60784.2023.00051
M3 - 会议稿件
AN - SCOPUS:85197523356
T3 - Proceedings - 2023 19th International Conference on Mobility, Sensing and Networking, MSN 2023
SP - 285
EP - 292
BT - Proceedings - 2023 19th International Conference on Mobility, Sensing and Networking, MSN 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 December 2023 through 16 December 2023
ER -