Optimization in Mobile Augmented Reality Systems for the Metaverse over Wireless Communications (2311.17630v1)
Abstract: As the essential technical support for Metaverse, Mobile Augmented Reality (MAR) has attracted the attention of many researchers. MAR applications rely on real-time processing of visual and audio data, and thus those heavy workloads can quickly drain the battery of a mobile device. To address such problem, edge-based solutions have appeared for handling some tasks that require more computing power. However, such strategies introduce a new trade-off: reducing the network latency and overall energy consumption requires limiting the size of the data sent to the edge server, which, in turn, results in lower accuracy. In this paper, we design an edge-based MAR system and propose a mathematical model to describe it and analyze the trade-off between latency, accuracy, server resources allocation and energy consumption. Furthermore, an algorithm named LEAO is proposed to solve this problem. We evaluate the performance of the LEAO and other related algorithms across various simulation scenarios. The results demonstrate the superiority of the LEAO algorithm. Finally, our work provides insight into optimization problem in edge-based MAR system for Metaverse.
- T. Tuan-Kiet, H.-T. HUYNH, D.-P. NGUYEN, L. Dinh-Dung, T. Thi-Hong, and Y. NAKASHIMA, “Demonstration of a visible light receiver using rolling-shutter smartphone camera,” in 2018 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2018, pp. 214–219.
- L. H. Lee, T. Braud, F. H. Bijarbooneh, and P. Hui, “Ubipoint: towards non-intrusive mid-air interaction for hardware constrained smart glasses,” in Proceedings of the 11th ACM Multimedia Systems Conference, 2020, pp. 190–201.
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
- P. Jain, J. Manweiler, and R. Roy Choudhury, “Low bandwidth offload for mobile ar,” in Proceedings of the 12th International on Conference on emerging Networking EXperiments and Technologies, 2016, pp. 237–251.
- T. Y.-H. Chen, L. Ravindranath, S. Deng, P. Bahl, and H. Balakrishnan, “Glimpse: Continuous, real-time object recognition on mobile devices,” in Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, 2015, pp. 155–168.
- R. Shea, A. Sun, S. Fu, and J. Liu, “Towards fully offloaded cloud-based ar: Design, implementation and experience,” in Proceedings of the 8th ACM on Multimedia Systems Conference, 2017, pp. 321–330.
- W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE internet of things journal, vol. 3, no. 5, pp. 637–646, 2016.
- M. Satyanarayanan, “The emergence of edge computing,” Computer, vol. 50, no. 1, pp. 30–39, 2017.
- L. Liu, H. Li, and M. Gruteser, “Edge assisted real-time object detection for mobile augmented reality,” in The 25th annual international conference on mobile computing and networking, 2019, pp. 1–16.
- H. Wang and J. Xie, “User preference based energy-aware mobile ar system with edge computing,” in IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 2020, pp. 1379–1388.
- D. Chatzopoulos, C. Bermejo, Z. Huang, and P. Hui, “Mobile augmented reality survey: From where we are to where we go,” Ieee Access, vol. 5, pp. 6917–6950, 2017.
- Q. Liu, S. Huang, J. Opadere, and T. Han, “An edge network orchestrator for mobile augmented reality,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 2018, pp. 756–764.
- J. Ahn, J. Lee, D. Niyato, and H.-S. Park, “Novel qos-guaranteed orchestration scheme for energy-efficient mobile augmented reality applications in multi-access edge computing,” IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 13 631–13 645, 2020.
- Z. Huang and V. Friderikos, “Proactive edge cloud optimization for mobile augmented reality applications,” in 2021 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2021, pp. 1–6.
- Y. He, J. Ren, G. Yu, and Y. Cai, “Optimizing the learning performance in mobile augmented reality systems with cnn,” IEEE Transactions on Wireless Communications, vol. 19, no. 8, pp. 5333–5344, 2020.
- H. A. Le Thi, T. Pham Dinh, and H. V. Ngai, “Exact penalty and error bounds in dc programming,” Journal of Global Optimization, vol. 52, no. 3, pp. 509–535, 2012.
- J. Zhao, L. Qian, and W. Yu, “Human-centric resource allocation in the metaverse over wireless communications,” arXiv preprint arXiv:2304.00355, 2023.
- M. Razaviyayn, “Successive convex approximation: Analysis and applications,” Ph.D. dissertation, University of Minnesota, 2014.
- A. Beck and L. Tetruashvili, “On the convergence of block coordinate descent type methods,” SIAM journal on Optimization, vol. 23, no. 4, pp. 2037–2060, 2013.