REFERENCES
1. Lei, S. M.; Chen, T. C.; Sun, M. T. Video bridging based on H.261 standard. IEEE. Trans. Circuits. Syst. Video. Technol. 1994, 4, 425-37.
3. Hartung, F.; Girod, B. Digital watermarking of MPEG-2 coded video in the bitstream domain. In 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, Munich, Germany. Apr 21-24, 1997. pp. 2621-4.
4. Marpe, D.; Wiegand, T.; Sullivan, G. The H.264/MPEG4 advanced video coding standard and its applications. IEEE. Commun. Mag. 2006, 44, 134-43.
5. Sullivan, G. J.; Ohm, J.; Han, W.; Wiegand, T. Overview of the high efficiency video coding (HEVC) standard. IEEE. Trans. Circuits. Syst. Video. Technol. 2012, 22, 1649-68.
6. Bross, B.; Wang, Y.; Ye, Y.; et al. Overview of the versatile video coding (VVC) standard and its applications. IEEE. Trans. Circuits. Syst. Video. Technol. 2021, 31, 3736-64.
7. Muhammad, M.; Kasmis, F.; De Cola, T. Advanced transport satellite protocol. In 2012 IEEE Global Communications Conference (GLOBECOM), Anaheim, USA. Dec 03-07, 2012. IEEE; 2012. pp. 3299-304.
8. Stockhammer, T. Dynamic adaptive streaming over HTTP -: standards and design principles. In Proceedings of the Second Annual ACM Conference on Multimedia Systems. Association for Computing Machinery; 2011. pp. 133-44.
9. Wang, D.; Peng, Y.; Ma, X.; et al. Adaptive wireless video streaming based on edge computing: opportunities and approaches. IEEE. Trans. Serv. Comput. 2019, 12, 685-97.
10. Souane, N.; Bourenane, M.; Douga, Y. Deep reinforcement learning-based approach for video streaming: dynamic adaptive video streaming over HTTP. Appl. Sci. 2023, 13, 11697.
11. Abou-Zeid, H.; Hassanein, H. S.; Valentin, S. Energy-efficient adaptive video transmission: exploiting rate predictions in wireless networks. IEEE. Trans. Veh. Technol. 2014, 63, 2013-26.
12. Zhou, L.; Wu, D.; Chen, J.; Dong, Z. Greening the smart cities: energy-efficient massive content delivery via D2D communications. IEEE. Trans. Ind. Inf. 2018, 14, 1626-34.
13. Min, X.; Duan, H.; Sun, W.; Zhu, Y.; Zhai, G. Perceptual video quality assessment: a survey. Sci. China. Inf. Sci. 2024, 67, 4133.
14. Zhao, T.; Liu, Q.; Chen, C. W. QoE in video transmission: a user experience-driven strategy. IEEE. Commun. Surv. Tutorials. 2017, 19, 285-302.
15. Antonakoglou, K.; Xu, X.; Steinbach, E.; Mahmoodi, T.; Dohler, M. Toward haptic communications over the 5G Tactile Internet. IEEE. Commun. Surv. Tutorials. 2018, 20, 3034-59.
16. Simpkins, A. Robotic tactile sensing: technologies and system (Dahiya, R.S. and Valle, M.; 2013) [On the Shelf]. IEEE. Robot. Automat. Mag. 2013, 20, 107.
17. Chen, C. C.; Chang, P. Z.; Shih, W. P. Flexible tactile sensor with high sensitivity utilizing botanical epidermal cell natural micro-structures. In SENSORS, 2012 IEEE, Taipei, Taiwan. Oct 28-31, 2012. IEEE; 2012. p. 1-4.
18. Rana, A.; Roberge, J.; Duchaine, V. An improved soft dielectric for a highly sensitive capacitive tactile sensor. IEEE. Sensors. J. 2016, 16, 7853-63.
19. Pyo, S.; Lee, J.; Kim, M.; et al. Development of a flexible three-axis tactile sensor based on screen-printed carbon nanotube-polymer composite. J. Micromech. Microeng. 2014, 24, 075012.
20. Liu, W.; Yu, P.; Gu, C.; Cheng, X.; Fu, X. Fingertip piezoelectric tactile sensor array for roughness encoding under varying scanning velocity. IEEE. Sensors. J. 2017, 17, 6867-79.
21. Massaro, A.; Spano, F.; Cazzato, P.; La Tegola, C.; Cingolani, R.; Athanassiou, A. Robot tactile sensing: gold nanocomposites as highly sensitive real-time optical pressure sensors. IEEE. Robot. Automat. Mag. 2013, 20, 82-90.
22. Fujiwara, E.; Paula, F. D.; Wu, Y. T.; Santos, M. F. M.; Schenkel, E. A.; Suzuki, C. K. Optical fiber tactile sensor based on fiber specklegram analysis. In 2017 25th Optical Fiber Sensors Conference (OFS), Jeju, Korea. Apr 24-28, 2017. IEEE; 2017. p. 1-4.
23. Alfadhel, A.; Kosel, J. Magnetic nanocomposite cilia tactile sensor. Adv. Mater. 2015, 27, 7888-92.
24. Yan, Y.; Hu, Z.; Yang, Z.; et al. Soft magnetic skin for super-resolution tactile sensing with force self-decoupling. Sci. Robot. 2021, 6, eabc8801.
25. Holland, O.; Steinbach, E.; Prasad, R. V.; et al. The IEEE 1918.1 “Tactile Internet” Standards Working Group and its Standards. Proc. IEEE. 2019, 107, 256-79.
26. Sakr, N.; Georganas, N. D.; Zhao, J. Human perception-based data reduction for haptic communication in Six-DoF telepresence systems. IEEE. Trans. Instrum. Meas. 2011, 60, 3534-46.
27. Xu, Y.; Huang, Q.; Zheng, Q.; Fang, Y.; Zhao, T. Perception-based prediction for efficient kinesthetic coding. IEEE. Signal. Process. Lett. 2024, 31, 2530-4.
28. Hassen, R.; Gulecyuz, B.; Steinbach, E. PVC-SLP: perceptual vibrotactile-signal compression based-on sparse linear prediction. IEEE. Trans. Multimedia. 2021, 23, 4455-68.
29. Steinbach, E.; Strese, M.; Eid, M.; et al. Haptic codecs for the Tactile Internet. Proc. IEEE. 2019, 107, 447-70.
30. Huang, K.; Lee, D. Consensus-based peer-to-peer control architecture for multiuser haptic interaction over the internet. IEEE. Trans. Robot. 2013, 29, 417-31.
31. Schuwerk, C.; Chaudhari, N.; Steinbach, E. An area-of-interest based communication architecture for shared haptic virtual environments. In 2013 IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE), Istanbul, Turkey. Oct 26-27, 2013. IEEE; 2013. pp. 57-62.
32. Ateya, A. A.; Vybornova, A.; Kirichek, R.; Koucheryavy, A. Multilevel cloud based Tactile Internet system. In 2017 19th International Conference on Advanced Communication Technology (ICACT), PyeongChang, Korea. Feb 19-22, 2017. IEEE; 2017. pp. 105-10.
33. Hu, Z.; Zheng, Z.; Wang, T.; Song, L.; Li, X. Caching as a service: small-cell caching mechanism design for service providers. IEEE. Trans. Wireless. Commun. 2016, 15, 6992-7004.
34. Ansari, N.; Sun, X. Mobile edge computing empowers Internet of Things. IEICE. Trans. Commun. 2018, E101.B, 604-19.
35. Kiani, A.; Ansari, N. Toward hierarchical mobile edge computing: an auction-based profit maximization approach. IEEE. Internet. Things. J. 2017, 4, 2082-91.
36. Hou, Z.; She, C.; Li, Y.; Niyato, D.; Dohler, M.; Vucetic, B. Intelligent communications for Tactile Internet in 6G: requirements, technologies, and challenges. IEEE. Commun. Mag. 2021, 59, 82-8.
37. Wei, X.; Duan, Q.; Zhou, L. A QoE-driven Tactile Internet architecture for smart city. IEEE. Network. 2020, 34, 130-6.
38. Kokkonis, G.; The Society of Digital Information and Wireless Communication. An open source architecture of a wireless body area network in a medical environment. Int. J. Digit. Inf. Wirel. Commun. 2016, 6, 63-77.
39. Gokhale, V.; Dabeer, O.; Chaudhuri, S. HoIP: haptics over Internet protocol. In 2013 IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE), Istanbul, Turkey. Oct 26-27, 2013. IEEE; 2013. pp. 45-50.
40. Gokhale, V.; Chaudhuri, S.; Dabeer, O. HoIP: a point-to-point haptic data communication protocol and its evaluation. In 2015 Twenty First National Conference on Communications (NCC), Mumbai, India. Feb 27 - Mar 01, 2015. IEEE; 2015. p. 1-6.
41. Kontogiannis, S.; Kokkonis, G. Proposed fuzzy real-time haptics protocol carrying haptic data and multisensory streams. Int. J. Comput. Commun. Control. 2020, 15.
42. Phung, H.; Hoang, P. T.; Jung, H.; Nguyen, T. D.; Nguyen, C. T.; Choi, H. R. Haptic display responsive to touch driven by soft actuator and soft sensor. IEEE/ASME. Trans. Mechatron. 2021, 26, 2495-505.
43. Uramune, R.; Ishizuka, H.; Hiraki, T.; Kawahara, Y.; Ikeda, S.; Oshiro, O. HaPouch: a miniaturized, soft, and wearable haptic display device using a liquid-to-gas phase change actuator. IEEE. Access. 2022, 10, 16830-42.
44. Zhu, L.; Jiang, X.; Shen, J.; Zhang, H.; Mo, Y.; Song, A. TapeTouch: a handheld shape-changing device for haptic display of soft objects. IEEE. Trans. Vis. Comput. Graph. 2022, 28, 3928-38.
45. Sakr, N.; Georganas, N. D.; Zhao, J. A perceptual quality metric for haptic signals. In 2007 IEEE International Workshop on Haptic, Audio and Visual Environments and Games, Ottawa, Canada. Oct 12-14, 2007. IEEE; 2007. pp. 27-32.
46. Chaudhari, R.; Steinbach, E.; Hirche, S. Towards an objective quality evaluation framework for haptic data reduction. In 2011 IEEE World Haptics Conference, Istanbul, Turkey. Jun 21-24, 2011. IEEE; 2011. pp. 539-44.
47. Hassen, R.; Steinbach, E. HSSIM: an objective haptic quality assessment measure for force-feedback signals. In 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), Cagliari, Italy. May 29 - Jun 01, 2018. IEEE; 2018. p. 1-6.
48. Liu, X.; Dohler, M.; Deng, Y. Vibrotactile quality assessment: hybrid metric design based on SNR and SSIM. IEEE. Trans. Multimedia. 2020, 22, 921-33.
49. She, C.; Yang, C.; Quek, T. Q. S. Radio resource management for ultra-reliable and low-latency communications. IEEE. Commun. Mag. 2017, 55, 72-8.
50. Nielsen, J. J.; Liu, R.; Popovski, P. Ultra-reliable low latency communication using interface diversity. IEEE. Trans. Commun. 2018, 66, 1322-34.
51. Kotaba, R.; Manchon, C. N.; Balercia, T.; Popovski, P. How URLLC can benefit from NOMA-based retransmissions. IEEE. Trans. Wireless. Commun. 2021, 20, 1684-99.
52. Tanveer, J.; Haider, A.; Ali, R.; Kim, A. An overview of reinforcement learning algorithms for handover management in 5G ultra-dense small cell networks. Appl. Sci. 2022, 12, 426.
53. Yuan, Y.; Yang, T.; Feng, H.; Hu, B. An iterative matching-stackelberg game model for channel-power allocation in D2D underlaid cellular networks. IEEE. Trans. Wireless. Commun. 2018, 17, 7456-71.
54. Zhang, S.; Liu, J.; Guo, H.; Qi, M.; Kato, N. Envisioning device-to-device communications in 6G. IEEE. Network. 2020, 34, 86-91.
55. Bennis, M.; Debbah, M.; Poor, H. V. Ultrareliable and low-latency wireless communication: tail, risk, and scale. Proc. IEEE. 2018, 106, 1834-53.
56. Yamaguchi, A.; Atkeson, C. G. Combining finger vision and optical tactile sensing: reducing and handling errors while cutting vegetables. In 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun, Mexico. Nov 15-17, 2016. IEEE; 2016; pp. 1045-51.
57. Yuan, W.; Dong, S.; Adelson, E. H. GelSight: high-resolution robot tactile sensors for estimating geometry and force. Sensors 2017, 17, 2762.
58. Donlon, E.; Dong, S.; Liu, M.; Li, J.; Adelson, E.; Rodriguez, A. GelSlim: a high-resolution, compact, robust, and calibrated tactile-sensing finger. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain. Oct 01-05, 2018. IEEE; 2018. pp. 1927-34.
59. Wang, S.; She, Y.; Romero, B.; Adelson, E. GelSight wedge: measuring high-resolution 3D contact geometry with a compact robot finger. In 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China. May 30 - Jun 05, 2021. IEEE; 2021. pp. 6468-75.
60. Gomes, D. F.; Lin, Z.; Luo, S. GelTip: a finger-shaped optical tactile sensor for robotic manipulation. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA. Oct 24 2020 - Jan 24 2021, 2021. IEEE; 2021. pp. 9903-9.
61. Fan, W.; Li, H.; Si, W.; Luo, S.; Lepora, N.; Zhang, D. ViTacTip: design and verification of a novel biomimetic physical vision-tactile fusion sensor. In 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan. May 13-17, 2024. IEEE; 2024. pp. 1056-62.
62. Kuppuswamy, N.; Alspach, A.; Uttamchandani, A.; Creasey, S.; Ikeda, T.; Tedrake, R. Soft-bubble grippers for robust and perceptive manipulation. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA. Oct 24 2020 - Jan 24 2021. IEEE; 2021. pp. 9917-24.
63. Zhang, L.; Wang, Y.; Jiang, Y. Tac3D: a novel vision-based tactile sensor for measuring forces distribution and estimating friction coefficient distribution. arXiv 2022, arXiv:2202.06211. https://arxiv.org/abs/2202.06211. (accessed 26 Jun 2025).
64. Liu, H.; Yu, Y.; Sun, F.; Gu, J. Visual–tactile fusion for object recognition. IEEE. Trans. Automat. Sci. Eng. 2017, 14, 996-1008.
65. Lee, J. T.; Bollegala, D.; Luo, S. “Touching to See” and “Seeing to Feel”: robotic cross-modal sensory data generation for visual-tactile perception. In 2019 International Conference on Robotics and Automation (ICRA), Montreal, Canada. May 20-24, 2019. IEEE; 2019. pp. 4276-82.
66. Wei, F.; Zhao, J.; Shan, C.; Yuan, Z. Alignment and multi-scale fusion for visual-tactile object recognition. In 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy. Jul 18-23, 2022. IEEE; 2022. p. 1-8.
67. Babadian, R. P.; Faez, K.; Amiri, M.; Falotico, E. Fusion of tactile and visual information in deep learning models for object recognition. Inf. Fusion. 2023, 92, 313-25.
68. Falco, P.; Lu, S.; Natale, C.; Pirozzi, S.; Lee, D. A transfer learning approach to cross-modal object recognition: from visual observation to robotic haptic exploration. IEEE. Trans. Robot. 2019, 35, 987-98.
69. Zhou, L.; Wu, D.; Chen, J.; Wei, X. Cross-modal collaborative communications. IEEE. Wireless. Commun. 2020, 27, 112-7.
70. Yang, L.; Wu, D.; Zhou, L. Heterogeneous stream scheduling for cross-modal transmission. IEEE. Trans. Commun. 2021, 69, 6037-49.
71. Wu, D.; Zhou, L. Cross-modal stream transmission: architecture, strategy, and technology. IEEE. Wireless. Commun. 2024, 31, 134-40.
72. Tong, Q.; Wei, W.; Liu, C.; Zhou, X.; Zhang, Y.; Wang, D. Cross-modal transmission with active packet loss and restoration for Tactile Internet. IEEE. Commun. Mag. 2024, 62, 70-6.
73. Wei, X.; Liao, J.; Zhou, L.; Sari, H.; Zhuang, W. Toward generic cross-modal transmission strategy. IEEE. Trans. Commun. 2024, 72, 6059-72.
74. Suo, Y.; Chen, Y.; Gao, Y.; Wei, X. Dynamic transmission mode selection for multi-modal services. IEEE. Commun. Lett. 2023, 27, 911-5.
75. Li, L.; Shi, D.; Hou, R.; Chen, R.; Lin, B.; Pan, M. Energy-efficient proactive caching for adaptive video streaming via data-driven optimization. IEEE. Int. Things. J. 2020, 7, 5549-61.
76. Li, C.; Toni, L.; Zou, J.; Xiong, H.; Frossard, P. QoE-driven mobile edge caching placement for adaptive video streaming. IEEE. Trans. Multimedia. 2018, 20, 965-84.
77. Chen, M.; Hao, Y.; Hu, L.; Hossain, M. S.; Ghoneim, A. Edge-CoCaCo: toward joint optimization of computation, caching, and communication on edge cloud. IEEE. Wireless. Commun. 2018, 25, 21-7.
78. Gao, Y.; Wei, X.; Kang, B.; Chen, J. Edge intelligence empowered cross-modal streaming transmission. IEEE. Network. 2021, 35, 236-43.
79. Yuan, Z.; Wei, X.; Zhou, L.; Zhuang, W. Content-aware cross-modal stream transmission. IEEE. Wireless. Commun. Lett. 2024, 13, 2507-11.
80. Gao, Y.; Wang, T.; Zhou, L.; Zhuang, W. CRoss-MODAL communications for holographic video streaming. IEEE. Wireless. Commun. 2025, 32, 96-102.
81. Wei, X.; Shi, Y.; Zhou, L. Haptic signal reconstruction for cross-modal communications. IEEE. Trans. Multimedia. 2022, 24, 4514-25.
82. Chen, M.; Xie, Y. Cross-modal reconstruction for tactile signal in human-robot interaction. Sensors 2022, 22, 6517.
83. Yang, Z.; Wang, H.; Shi, Y.; Ye, L.; Wei, X. Fine-grained audio-visual aided haptic signal reconstruction. IEEE. Signal. Process. Lett. 2024, 31, 1349-53.
84. Chen, Y.; Li, A.; Wu, D.; Zhou, L. Toward general cross-modal signal reconstruction for robotic teleoperation. IEEE. Trans. Multimedia. 2024, 26, 3541-53.
85. Wei, X.; Yao, Y.; Wang, H.; Zhou, L. Perception-aware cross-modal signal reconstruction: from audio-haptic to visual. IEEE. Trans. Multimedia. 2023, 25, 5527-38.
86. Wei, X.; Zhou, L. AI-enabled cross-modal communications. IEEE. Wireless. Commun. 2021, 28, 182-9.
87. Farooq, A.; Rantala, J.; Raisamo, R.; Hippula, A. Haptic mediation through artificial intelligence: magnetorheological fluid as vibrotactile signal mediator. In 2022 Symposium on Design, Test, Integration and Packaging of MEMS/MOEMS (DTIP), Pont-a-Mousson, France. Jul 11-13, 2022. IEEE; 2022. p. 1-4.
88. Cheng, L.; Zhang, H.; Di, B.; Niyato, D.; Song, L. Large language models empower multimodal integrated sensing and communication. IEEE. Commun. Mag. 2025, 63, 190-7.
89. Lipkova, J.; Chen, R. J.; Chen, B.; et al. Artificial intelligence for multimodal data integration in oncology. Cancer. Cell. 2022, 40, 1095-110.
90. Shao, J.; Ma, J.; Zhang, Q.; Li, W.; Wang, C. Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin. Cancer. Biol. 2023, 91, 1-15.
91. Wang, T.; Zhang, B.; Jiang, D.; Li, D. A multimodal large language model framework for intelligent perception and decision-making in smart manufacturing. Sensors 2025, 25, 3072.
92. Sanfilippo, F.; Blažauskas, T.; Girdžiūna, M.; Janonis, A.; Kiudys, E.; Salvietti, G. A multi-modal auditory-visual-tactile e-learning framework. In: Sanfilippo F, Granmo O, Yayilgan SY, Bajwa IS, editors. Intelligent technologies and applications. Cham: Springer International Publishing; 2022. pp. 119-31.
93. Xu, C.; Zhou, Y.; He, B.; et al. An active strategy for safe human–robot interaction based on visual–tactile perception. IEEE. Syst. J. 2023, 17, 5555-66.
