TY - JOUR
T1 - Netvision
T2 - On-demand video processing in wireless networks
AU - Lu, Zongqing
AU - Chan, Kevin
AU - Urgaonkar, Rahul
AU - Pu, Shiliang
AU - La Porta, Thomas
N1 - Funding Information:
Manuscript received September 6, 2018; revised June 13, 2019 and July 29, 2019; accepted November 18, 2019; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Editor S. Rao. Date of publication December 11, 2019; date of current version February 14, 2020. This work was supported in part by the Network Science CTA under Grant W911NF-09-2-0053, in part by the NSF China under Grant 61872009, and in part by Hikvision. A preliminary version of this work appeared in the Proceedings of IEEE ICNP 2016. (Corresponding author: Zongqing Lu.) Z. Lu is with the Department of Computer Science, Peking University, Beijing 100871, China (e-mail: zongqing.lu@pku.edu.cn).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - The vast adoption of mobile devices with cameras has greatly contributed to the proliferation of the creation and distribution of videos. For a variety of purposes, valuable information may be extracted from these videos. While the computational capability of mobile devices has greatly improved recently, video processing is still a demanding task for mobile devices. We design an on-demand video processing system, NetVision, that performs distributed video processing using deep learning across a wireless network of mobile and edge devices to answer queries while minimizing the query response time. However, the problem of minimal query response time for processing videos stored across a network is a strongly NP-hard problem. To deal with this, we design a greedy algorithm with bounded performance. To further deal with the dynamics of the transmission rate between mobile and edge devices, we design an adaptive algorithm. We built NetVision and deployed it on a small testbed. Based on the measurements of the testbed and by extensive simulations, we show that the greedy algorithm is close to the optimum and the adaptive algorithm performs better with more dynamic transmission rates. We then perform experiments on the small testbed to examine the realized system performance in both stationary networks and mobile networks.
AB - The vast adoption of mobile devices with cameras has greatly contributed to the proliferation of the creation and distribution of videos. For a variety of purposes, valuable information may be extracted from these videos. While the computational capability of mobile devices has greatly improved recently, video processing is still a demanding task for mobile devices. We design an on-demand video processing system, NetVision, that performs distributed video processing using deep learning across a wireless network of mobile and edge devices to answer queries while minimizing the query response time. However, the problem of minimal query response time for processing videos stored across a network is a strongly NP-hard problem. To deal with this, we design a greedy algorithm with bounded performance. To further deal with the dynamics of the transmission rate between mobile and edge devices, we design an adaptive algorithm. We built NetVision and deployed it on a small testbed. Based on the measurements of the testbed and by extensive simulations, we show that the greedy algorithm is close to the optimum and the adaptive algorithm performs better with more dynamic transmission rates. We then perform experiments on the small testbed to examine the realized system performance in both stationary networks and mobile networks.
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U2 - 10.1109/TNET.2019.2954909
DO - 10.1109/TNET.2019.2954909
M3 - Article
AN - SCOPUS:85079801777
SN - 1063-6692
VL - 28
SP - 196
EP - 209
JO - IEEE/ACM Transactions on Networking
JF - IEEE/ACM Transactions on Networking
IS - 1
M1 - 8931010
ER -