AI-Driven Video Content Optimization Strategies for Immersive Media

Da Xu

doi:10.71222/vgbtbv67

Authors

Da Xu Video Infra, Meta, Menlo Park, CA, 94025, United States Author

DOI:

https://doi.org/10.71222/vgbtbv67

Keywords:

immersive media, artificial intelligence, video content optimization

Abstract

With the rapid development of immersive media technology, users' demand for video content interactivity, immersion, and intelligent presentation is constantly increasing. The advantages of AI technology in content perception, creation, integration, and transmission are gradually becoming prominent, and it is an important support for promoting the iterative upgrading of immersive media. This article starts with the basic theory of immersive media and AI driven video content optimization, and constructs an integrated system framework covering perception analysis, multimodal generation, rendering and transmission. It discusses the adaptability of existing technology algorithms, the complexity of polymorphic processing, and the bottleneck of terminal adaptation, and provides solutions such as semantic parsing enhancement, polymorphic fusion optimization, cloud edge collaborative rendering, etc., in order to provide theoretical reference and practical path for promoting the content experience improvement of immersive media and creating intelligent applications.

References

1. Y. Yang, S. N. Sannusi, and A. R. Ahmad Rizal, "Exploring the role of immersive media technologies in environmental communication: A case study of underwater earth projects," Environmental Communication, vol. 19, no. 3, pp. 432-448, 2025. doi: 10.1080/17524032.2024.2420785

2. H. K. Ravuri, J. Struye, J. van der Hooft, T. Wauters, F. De Turck, J. Famaey, and M. Torres Vega, "Context-Aware and Reliable Transport Layer Framework for Interactive Immersive Media Delivery Over Millimeter Wave," Journal of Network and Systems Management, vol. 32, no. 4, p. 78, 2024. doi: 10.1007/s10922-024-09845-5

3. P. H. Gueldner, K. E. Kerr, N. Liang, T. K. Chung, T. Tallarita, J. Wildenberg, and I. Sen, "Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study," Journal of Vascular Surgery Cases, Innovations and Techniques, vol. 11, no. 4, p. 101806, 2025. doi: 10.1016/j.jvscit.2025.101806

4. M. M. Hasan, M. Pramanik, I. Alam, A. Kumar, R. Avtar, and M. Zhran, "Assessing the efficacy of artificial intelligence based city-scale blue green infrastructure mapping using Google Earth Engine in the Bangkok metropolitan region," Journal of Urban Management, 2024. doi: 10.1016/j.jum.2024.11.009

5. X. Hui, S. H. Raza, S. W. Khan, U. Zaman, and E. C. Ogadimma, "Exploring regenerative tourism using media richness theory: emerging role of immersive journalism, metaverse-based promotion, eco-literacy, and pro-environmental behavior," Sustainability, vol. 15, no. 6, p. 5046, 2023. doi: 10.3390/su15065046

6. A. Sanatizadeh, Y. Lu, K. Zhao, and Y. Hu, "Engagement or entanglement? The dual impact of generative artificial intelligence in online knowledge exchange platforms," Information & Management, 2025. doi: 10.1016/j.im.2025.104178