TY - GEN
T1 - Pricing in the Open Market of Crowdsourced Video Edge Caching
T2 - 2022 IEEE International Performance, Computing, and Communications Conference, IPCCC 2022
AU - Wang, Xueqing
AU - Wang, Liang
AU - Yu, Zhiwen
AU - Xu, Zichuan
AU - Zhang, Yao
AU - Chu, Weibo
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - By placing popular contents on the network edges, edge caching becomes a promising technique to improve the quality of experience (QoE) of the end users and reduce backhaul link congestion. In this paper, we examine an open market of crowdsourced video edge caching, where within each time slot, the newcome private edge devices strategically declare their own bids to the Video Content Provider (VCP) operator for contributions; and the operator optimally recruits caching devices among the newcome and existing served devices to maximize the expected QoE, under a budget constraint. From the perspective of newcome edge devices, we propose and study a novel pricing problem, namely Pri-CVEC, to determine the bid prices for profit maximization. The problem is challenging due to the importing of strategic interactions between the newcome devices and the VCP operator, and competition between the newcome and the existing served devices. We formulate it as a stackelberg knapsack problem. By leveraging the dynamic programming and linear programming-relaxation method, we propose Pri-DP and Pri- LPR algorithm, respectively.
AB - By placing popular contents on the network edges, edge caching becomes a promising technique to improve the quality of experience (QoE) of the end users and reduce backhaul link congestion. In this paper, we examine an open market of crowdsourced video edge caching, where within each time slot, the newcome private edge devices strategically declare their own bids to the Video Content Provider (VCP) operator for contributions; and the operator optimally recruits caching devices among the newcome and existing served devices to maximize the expected QoE, under a budget constraint. From the perspective of newcome edge devices, we propose and study a novel pricing problem, namely Pri-CVEC, to determine the bid prices for profit maximization. The problem is challenging due to the importing of strategic interactions between the newcome devices and the VCP operator, and competition between the newcome and the existing served devices. We formulate it as a stackelberg knapsack problem. By leveraging the dynamic programming and linear programming-relaxation method, we propose Pri-DP and Pri- LPR algorithm, respectively.
KW - Crowdsourced edge caching
KW - pricing policy
KW - quality of experience
KW - stackelberg knapsack
UR - http://www.scopus.com/inward/record.url?scp=85147142427&partnerID=8YFLogxK
U2 - 10.1109/IPCCC55026.2022.9894319
DO - 10.1109/IPCCC55026.2022.9894319
M3 - 会议稿件
AN - SCOPUS:85147142427
T3 - Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference
SP - 263
EP - 268
BT - 2022 IEEE International Performance, Computing, and Communications Conference, IPCCC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 November 2022 through 13 November 2022
ER -