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Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services (2208.05438v6)

Published 10 Aug 2022 in cs.AI

Abstract: Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users' QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme. The code for this paper is available at https://github.com/HongyangDu/AttentionQoE

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References (56)
  1. J. Joshua, “Information bodies: Computational anxiety in neal stephenson’s snow crash,” Interdiscip. Lit. Stud., vol. 19, no. 1, pp. 17–47, Jan. 2017.
  2. H. Du, J. Zhang, K. Guan, D. Niyato, H. Jiao, Z. Wang, and T. Kürner, “Performance and optimization of reconfigurable intelligent surface aided THz communications,” IEEE Trans. Commun., vol. 70, no. 5, pp. 3575–3593, May 2022.
  3. S. Verma, Y. Kawamoto, and N. Kato, “A smart Internet-wide port scan approach for improving IoT security under dynamic WLAN environments,” IEEE Internet Things J., vol. 9, no. 14, pp. 11 951–11 961, 2021.
  4. ——, “A network-aware Internet-wide scan for security maximization of IPV6-enabled WLAN IoT devices,” IEEE Internet Things J., vol. 8, no. 10, pp. 8411–8422, 2020.
  5. C. She, C. Sun, Z. Gu, Y. Li, C. Yang, H. V. Poor, and B. Vucetic, “A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning,” Proc. IEEE, vol. 109, no. 3, pp. 204–246, Mar. 2021.
  6. W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Netw., vol. 34, no. 3, pp. 134–142, Mar. 2019.
  7. C. She, R. Dong, Z. Gu, Z. Hou, Y. Li, W. Hardjawana, C. Yang, L. Song, and B. Vucetic, “Deep learning for ultra-reliable and low-latency communications in 6G networks,” IEEE Netw., vol. 34, no. 5, pp. 219–225, May 2020.
  8. V. Kelkkanen, M. Fiedler, and D. Lindero, “Bitrate requirements of non-panoramic VR remote rendering,” in Proc. ACM Int. Conf. Multimedia, Oct. 2020, pp. 3624–3631.
  9. Y. Lu, H. Zheng, S. Chand, W. Xia, Z. Liu, X. Xu, L. Wang, Z. Qin, and J. Bao, “Outlook on human-centric manufacturing towards industry 5.0,” J. Manuf. Syst., vol. 62, pp. 612–627, Jan. 2022.
  10. R. Dong, C. She, W. Hardjawana, Y. Li, and B. Vucetic, “Deep learning for radio resource allocation with diverse quality-of-service requirements in 5G,” IEEE Trans. Wireless Commun., vol. 20, no. 4, pp. 2309–2324, Apr. 2020.
  11. Y. Han, D. Niyato, C. Leung, C. Miao, and D. I. Kim, “A dynamic resource allocation framework for synchronizing metaverse with IoT service and data,” arXiv preprint arXiv:2111.00431, 2021.
  12. C. She, P. Cheng, A. Li, and Y. Li, “Grand challenges in signal processing for communications,” Front. Signal Process., vol. 1, p. 664331, 2021.
  13. H. Du, B. Ma, D. Niyato, and J. Kang, “Rethinking quality of experience for metaverse services: A consumer-based economics perspective,” arXiv preprint arXiv:2208.01076, 2022.
  14. W. Yang, H. Du, Z. Liew, W. Y. B. Lim, Z. Xiong, D. Niyato, X. Chi, X. S. Shen, and C. Miao, “Semantic communications for 6G future internet: Fundamentals, applications, and challenges,” arXiv preprint arXiv:2207.00427, 2022.
  15. H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, J. Zhang et al., “Semantic communications for wireless sensing: RIS-aided encoding and self-supervised decoding,” arXiv preprint arXiv:2211.12727, 2022.
  16. Y. Zhang, Y. Gu, M. Pan, N. H. Tran, Z. Dawy, and Z. Han, “Multi-dimensional incentive mechanism in mobile crowdsourcing with moral hazard,” IEEE Trans. Mobile Comput., vol. 17, no. 3, pp. 604–616, Mar. 2017.
  17. H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, X. S. Shen, and D. I. Kim, “Exploring attention-aware network resource allocation for customized metaverse services,” IEEE Netw., to appear, 2022.
  18. A. C. Schütz, D. I. Braun, and K. R. Gegenfurtner, “Eye movements and perception: A selective review,” J. Vision, vol. 11, no. 5, pp. 9–9, May 2011.
  19. S. Onat, A. Açık, F. Schumann, and P. König, “The contributions of image content and behavioral relevancy to overt attention,” PLoS One, vol. 9, no. 4, p. e93254, Apr. 2014.
  20. E. Bozkir, D. Geisler, and E. Kasneci, “Assessment of driver attention during a safety critical situation in VR to generate VR-based training,” in ACM Symp. Appl. Percept., Sept. 2019, pp. 1–5.
  21. S. Berkovsky, R. Taib, I. Koprinska, E. Wang, Y. Zeng, J. Li, and S. Kleitman, “Detecting personality traits using eye-tracking data,” in Proc. CHI Conf. Hum. Factors Comput. Syst., Mar. 2019, pp. 1–12.
  22. C. Braunagel, D. Geisler, W. Rosenstiel, and E. Kasneci, “Online recognition of driver-activity based on visual scanpath classification,” IEEE Trans. Intell. Transp. Syst., vol. 9, no. 4, pp. 23–36, Apr. 2017.
  23. Y. Feng, G. Cheung, W.-t. Tan, P. Le Callet, and Y. Ji, “Low-cost eye gaze prediction system for interactive networked video streaming,” IEEE Trans. Multimedia, vol. 15, no. 8, pp. 1865–1879, Aug. 2013.
  24. G. Ghinea and G.-M. Muntean, “An eye-tracking-based adaptive multimedia streaming scheme,” in Proc. IEEE Int. Conf. Multimedia Expo., 2009, pp. 962–965.
  25. Y. Su, Y. Xu, S. Cheng, C. Ko, and K.-Y. Young, “Development of an effective 3D VR-based manipulation system for industrial robot manipulators,” in Proc. Asian Control Conf. (ASCC).   IEEE, 2019, pp. 1–6.
  26. R. Likamwa, J. Hu, V. Kodukula, and Y. Liu, “Adaptive resolution-based tradeoffs for energy-efficient visual computing systems,” IEEE Pervasive Comput., to apper, 2021.
  27. Y. Zhang, M. Pan, L. Song, Z. Dawy, and Z. Han, “A survey of contract theory-based incentive mechanism design in wireless networks,” IEEE Wireless Commun., vol. 24, no. 3, pp. 80–85, Mar. 2017.
  28. L. Gao, X. Wang, Y. Xu, and Q. Zhang, “Spectrum trading in cognitive radio networks: A contract-theoretic modeling approach,” IEEE J. Sel. Areas Commun., vol. 29, no. 4, pp. 843–855, Apr. 2011.
  29. Y. Zhang, L. Song, W. Saad, Z. Dawy, and Z. Han, “Contract-based incentive mechanisms for device-to-device communications in cellular networks,” IEEE J. Sel. Areas Commun., vol. 33, no. 10, pp. 2144–2155, Oct. 2015.
  30. L. Lv, C. Zheng, L. Zhang, C. Shan, Z. Tian, X. Du, and M. Guizani, “Contract and lyapunov optimization-based load scheduling and energy management for UAV charging stations,” IEEE Trans. Green Commun. Netw., vol. 5, no. 3, pp. 1381–1394, Mar. 2021.
  31. K. Wang, W. Chen, J. Li, Y. Yang, and L. Hanzo, “Joint task offloading and caching for massive MIMO-aided multi-tier computing networks,” IEEE Trans. Commun., to appear, 2022.
  32. T. Um, H. Kim, H. Kim, J. Lee, C. Koo, and N. Chung, “Travel incheon as a metaverse: Smart tourism cities development case in Korea,” in ENTER22 E-Tourism Conf.   Springer, Cham, 2022, pp. 226–231.
  33. C. Tsao and P.-C. Su, “A technical report for visual attention estimation in HMD challenge,” in Prof. Int. Conf. AIVR, 2021, pp. 143–144.
  34. L. Zhang, Y. Li, and L. J. Cimini, “Statistical performance analysis for MIMO beamforming and STBC when co-channel interferers use arbitrary MIMO modes,” IEEE Trans. Commun., vol. 60, no. 10, pp. 2926–2937, Oct. 2012.
  35. R. A. Horn, “The hadamard product,” in Proc. Symp. Appl. Math, vol. 40, 1990, pp. 87–169.
  36. P. Yang, T. Q. Quek, J. Chen, C. You, and X. Cao, “Feeling of presence maximization: mmWave-enabled virtual reality meets deep reinforcement learning,” arXiv preprint arXiv:2107.01001, 2021.
  37. A. Nazir, S. Raza, and C.-N. Chuah, “Unveiling facebook: A measurement study of social network based applications,” in Proc. ACM SIGCOMM Int. Meas., 2008, pp. 43–56.
  38. J. Tremewan, “Behavioral economics: Toward a new economics by integration with traditional economics.” Econ. Rec., vol. 96, no. 313, pp. 221–221, 2020.
  39. H. Du, D. Niyato, J. Kang, D. I. Kim, and C. Miao, “Optimal targeted advertising strategy for secure wireless edge metaverse,” arXiv preprint arXiv:2111.00511, 2021.
  40. J.-S. Pang and M. Fukushima, “Quasi-variational inequalities, generalized nash equilibria, and multi-leader-follower games,” Comput. Manag. Sci., vol. 2, no. 1, pp. 21–56, Jan. 2005.
  41. S. Dehaene, “The neural basis of the Weber-Fechner law: A logarithmic mental number line,” Trends Cogn. Sci., vol. 7, no. 4, pp. 145–147, Apr. 2003.
  42. S. Bouchard, J. St-Jacques, G. Robillard, and P. Renaud, “Anxiety increases the feeling of presence in virtual reality,” Presence: Teleop. Virt. Environ., vol. 17, no. 4, pp. 376–391, Apr. 2008.
  43. P. Reichl, B. Tuffin, and R. Schatz, “Logarithmic laws in service quality perception: Where microeconomics meets psychophysics and quality of experience,” Telecommun. Syst., vol. 52, no. 2, pp. 587–600, Feb 2013.
  44. P. Reichl, S. Egger, R. Schatz, and A. D’Alconzo, “The logarithmic nature of QoE and the role of the Weber-Fechner law in QoE assessment,” in Proc. IEEE Int. Conf. Commun., May 2010, pp. 1–5.
  45. I. Lubashevsky, “Psychophysical laws as reflection of mental space properties,” Phys. Life Rev., vol. 31, pp. 276–303, 2019.
  46. T. Nishio, R. Shinkuma, T. Takahashi, and N. B. Mandayam, “Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud,” in Proc. Int. Mobile Cloud Comput. & Netw. Workshop, 2013, pp. 19–26.
  47. Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009.
  48. Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in Proc. IEEE Intl. Conf. Data Min., Dec. 2008, pp. 263–272.
  49. D. Zachariah, M. Sundin, M. Jansson, and S. Chatterjee, “Alternating least-squares for low-rank matrix reconstruction,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 231–234, Apr. 2012.
  50. S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme, “Fast context-aware recommendations with factorization machines,” in Proc. Intl. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2011, pp. 635–644.
  51. X. He, H. Zhang, M.-Y. Kan, and T.-S. Chua, “Fast matrix factorization for online recommendation with implicit feedback,” in Proc. Intl. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2016, pp. 549–558.
  52. Huawei iLab, “Cloud VR network solution white paper,” https://www.huawei.com/en/news/2018/9/cloud-vr-solution-white-paper.
  53. A. Chorti, A. N. Barreto, S. Köpsell, M. Zoli, M. Chafii, P. Sehier, G. Fettweis, and H. V. Poor, “Context-aware security for 6G wireless: The role of physical layer security,” IEEE Commun. Standards Mag., vol. 6, no. 1, pp. 102–108, Jan. 2022.
  54. Wolfram, “The wolfram functions site,” http://functions.wolfram.com.
  55. H. Du, J. Zhang, J. Cheng, and B. Ai, “Millimeter wave communications with reconfigurable intelligent surfaces: Performance analysis and optimization,” IEEE Trans. Commun., vol. 69, no. 4, pp. 2752–2768, Apr. 2021.
  56. S. S. Dragomir and N. M. Ionescu, “Some converse of Jensen’s inequality and applications,” Revue d’Analyse Numérique et de Théorie de l’Approximation, vol. 23, no. 1, pp. 71–78, Jan. 1994.
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