VEGA-360: VIEWPORT-AWARE HIERARCHICAL GROUPED ALLOCATION FOR MULTI-LAYER 360° VIDEO STREAMING

(Received: 26-Nov.-2025, Revised: 13-Jan.-2026 , Accepted: 16-Jan.-2026)

Authors Phi Dinh Huynh, Nguyen Viet Hung,

Keywords #VEGA-360 #QoE #360-degree video #SHVC encoding #Virtual reality

Abstract Delivering multi-layer, tiled 360° video to multiple wireless users is challenging due to limited radio resources, heterogeneous channel conditions and a strong viewport-dependent quality of experience (QoE). To address this problem, we propose Viewport-Enhanced Grouped Allocation for 360° Video (VEGA-360), a hierarchical viewport-aware resource allocation framework for multi-user 360° video streaming. VEGA-360 adopts a two-stage design. On the main stage, VEGA-360 partitions users into a small number of clusters using a joint criterion that combines spectral efficiency and viewport similarity derived from viewport weights and allocates a per-cluster resource budget accordingly. In the fine-tuning phase, VEGA-360 solves independent optimization sub-problems per cluster at the tile granularity, enforcing radio resource and SHVC scalability constraints while maximizing a utility metric that accounts for viewport-weighted visual quality and the transmission overhead caused by distributing multiple tile instances. By separating coarse-grained grouping and budgeting decisions from fine-grained tile-layer allocation, VEGA-360 reduces the size of each optimization instance and improves computational tractability while maintaining viewport-aware service in dense multi-user scenarios. Simulation results show that VEGA-360 achieves competitive utility/QoE compared to a monolithic MILP baseline, with substantially shorter solution times.

References

[1] A. J. Nair et al., "Unleashing Digital Frontiers: Bridging Realities of Augmented Reality, Virtual Realityand the Metaverse," The Metaverse Dilemma: Challenges and Opportunities for Business and Society, Emerald Publishing Limited, p. 122024, DOI: 10.1108/978-1-83797-524-220241006, 2024.

[2] J. Tu et al., "Pstile: Perception-sensitivity-based 360∘ Tiled Video Streaming for Industrial Surveillance,"IEEE Transactions on Industrial Informatics, vol. 19, no. 9, pp. 9777-9789, 2023.

[3] K. K. Sreedhar et al., "Viewport-adaptive Encoding and Streaming of 360-degree Video for VirtualReality Applications," Proc. of the 2016 IEEE (ISM), pp. 583-586, San Jose, USA, 2016.

[4] C.-H. Yeh et al., "Fast Prediction for Quality Scalability of High Efficiency Video Coding ScalableExtension," Journal of Visual Communication and Image Representation, vol. 58, pp. 462-476, 2019.

[5] J. M. Boyce et al., "Overview of SHVC: Scalable Extensions of the High Efficiency Video CodingStandard," IEEE Trans. on Circuits and Systems for Video Technology, vol. 26, no. 1, pp. 20-34, 2015.

[6] M. J. Mohammed et al., "A Comparison of 4G LTE and 5G Network Cybersecurity Performance," Proc.of the 2024 35th Conf. of Open Innovations Association (FRUCT), pp. 452-464, Tampere, Finland, 2024.

[7] L. Sun et al., "Multi-path Multi-tier 360-degree Video Streaming in 5G Networks," Proc. of the 9th ACMMultimedia Systems Conf., pp. 162-173, DOI: 10.1145/3204949.3204978, 2018.

[8] N. Al-Najdawi, "High Performance Block Matching Algorithm for High Bit-rate Real-time VideoCommunication," Jordanian J. of Computers and Inf. Tech. (JJCIT), vol. 4, no. 1, pp. 10-24, 2018.

[9] N. V. Hung et al., "LVSUM-Optimized Live 360 Degree Video Streaming in Unicast and Multicast overMobile Networks," Proc. of the 2023 IEEE 15th Int. Conf. on Computational Intelligence and Communication Networks (CICN), pp. 29-34, Bangkok, Thailand, 2023.

[10] D. Nguyen, N. V. Hung, N. T. Phong, T. T. Huong and T. C. Thang, "Scalable Multicast for Live 360-degree Video Streaming over Mobile Networks," IEEE Access, vol. 10, pp. 38802-38 812, 2022.

[11] D. T. Nguyen, T. H. Tran and V. H. Nguyen, "Mellifluous Viewport Bitrate Adaptation for 360∘ VideosStreaming over HTTP/2," ICT Research, [Online], Available: https: //api.semanticscholar.org/CorpusID: 273740657, 2024.

[12] N. V. Hung, B. D. Tien, T. T. T. Anh, P. N. Nam and T. T. Huong, "An Efficient Approach to Terminate360-video Stream on HTTP/3," Proc. of AIP Conf. Proc., vol. 2909, no. 1, p. 050004, AIP Publish, 2023.

[13] X. Feng, W. Li and S. Wei, "LiveROI: Region of Interest Analysis for Viewport Prediction in Live MobileVirtual Reality Streaming," Proc. of the 12th ACM Multimedia Systems Conf., pp. 132-145, DOI: 10.1145/3458305.3463378, 2021.

[14] N. Hung et al., "Building an Online Learning Model through a Dance Recognition Video Based on DeepLearning," Informatics and Automation, vol. 23, no. 1, pp. 101-128, 2024.

[15] M. R. Civanlar, Proc. of the 30th ACM Workshop on Network and Operating Systems Support for DigitalAudio and Video, ser. ACM Conferences, New York, NY: Association for Computing Machinery, 2020.

[16] M. Mahmoud et al., "Optimized Tile Quality Selection in Multi-user 360∘ Video Streaming," IEEE OpenJournal of the Communications Society, vol. 5, pp. 7301-7316, 2024.

[17] Y. Chen et al., "Streaming 360∘ VR Video with Statistical QoS Provisioning in mmWave Networks fromDelay and Rate Perspectives," IEEE Trans. on Wireless Comm., vol. 24, no. 6, pp. 4721-4737, 2025.

[18] B. Badnava, J. Chakareski and M. Hashemi, "Multi-task Decision-making for Multi-user 360 VideoProcessing over Wireless Networks," Proc. of the 2024 IEEE 7th Int. Conf. on Multimedia Information Processing and Retrieval (MIPR), pp. 294-300, DOI: 10.1109/MIPR62202.2024.00054, 2024.

[19] H. Wang, Z. Long, H. Dong and A. El Saddik, "MADRL-based Rate Adaptation for 360∘ VideoStreaming with Multi-viewpoint Prediction," IEEE IoT J., vol. 11, no. 15, p. 26503-26517, Aug. 2024.

[20] V. H. Nguyen et al., "Scalable and Resilient 360-degree-video Adaptive Streaming over HTTP/2 againstSudden Network Drops," Computer Communications, vol. 216, pp. 1-15, 2024.

[21] H. Guo et al., "Joint Adaptation for Mobile 360-degree Video Streaming and Enhancement," IEEETransactions on Mobile Computing, vol. 24, no. 8, pp. 7726-7741, 2025.

[22] W. Feng, S. Wang and Y. Dai, "Adaptive 360-degree Streaming: Optimizing with Multi-window andStochastic Viewport Prediction," IEEE Trans. on Mobile Computing, vol. 24, no. 7, pp. 5903-5915, 2025.

[23] J. Lee, H. Lu and Y. Chen, "Robust Wireless VR Video Transmission Based on Overlapped FoVs," Proc. of the IEEE Int. Conf. on Communications (ICC 2023), pp. 3084-3089, Rome, Italy, 2023.

[24] M. N. Ehsan Abedini, "Cubic-learn: A Reinforcement Learning Approach to Cubic Congestion Control,"Jordanian Journal of Computers and Information Technology (JJCIT), vol. 11, no. 4, pp. 466 483, 2025.

[25] M. Z. A. Wahba, S. Baldoni and F. Battisti, "Learning-based Viewport Prediction for 360-degree Videos:A Review," Electronics, vol. 14, no. 18, p. 3743, 2025.

[26] M. Wang et al., "CoLive: Edge-assisted Clustered Learning Framework for Viewport Prediction in 360∘Live Streaming," IEEE Transactions on Multimedia, vol. 26, pp. 5078-5091, 2024.

[27] L. Zhang et al., "Optimizing Mobile-friendly Viewport Prediction for Live 360-degree Video Streaming,"IEEE Transactions on Mobile Computing, vol. 24, no. 10, pp. 10441-10455, 2025.

[28] H. Nguyen et al., "An Accurate Viewport Estimation Method for 360 Video Streaming Using Deep Learning," EAI Endorsed Trans. on Industrial Networks and Intelligent Systems, vol. 9, no. 4, p. e2, 2022.

[29] N. V. Hung et al., "Building Predictive Smell Models for Virtual Reality Environments," ComputerScience and Automation, vol. 24, no. 2, pp. 556-582, 2025.

[30] N. V. Hung et al., "HEVERL: Head-eye Movement Oriented Viewport Estimation Based onReinforcement Learning," Journal on VR/Multimedia Systems, 2025, "Viewport Prediction for 360-degree Video Using Reinforcement Learning," Russian Academy of Sciences, 2025.

[31] Y. Wang et al., "Synergistic Temporal-spatial User-aware Viewport Prediction for Optimal Adaptive 360-degree Video Streaming," IEEE Transactions on Broadcasting, vol. 70, no. 2, pp. 453-467, 2024.

[32] J. Li, Y. Wang and Y. Liu, "Meta360: Exploring User-specific and Robust Viewport Prediction in 360-degree Videos through Bi-directional LSTM and Meta-adaptation," Proc. of the 2023 IEEE Int. Symposium on Mixed and Augmented Reality (ISMAR), pp. 652-661, Sydney, Australia, 2023.

[33] A. Yaqoob and G.-M. Muntean, "A Combined Field-of-view Prediction-assisted Viewport AdaptiveDelivery Scheme for 360∘ Videos," IEEE Trans. on Broadcasting, vol. 67, no. 3, pp. 746-760, 2021.

[34] Z. Li, Y. Wang, Y. Liu, J. Li and P. Zhu, "JUST360: Optimizing 360-degree Video Streaming Systemswith Joint Utility," IEEE Transactions on Broadcasting, vol. 70, no. 2, pp. 468-481, 2024.

[35] C. Wu, Y. Chen, Y. Chen, F. Guo, X. Qin and H. Lu, "Physiological Signal-driven QoE Optimization forWireless Virtual Reality Transmission," arXiv: 2508.09151, DOI: 10.48550/arXiv.2508.09151, 2025.

[36] V. H. Nguyen et al., "Retina-based Quality Assessment of Tile-coded 360-degree Videos," EAI EndorsedTransactions on Industrial Networks and Intelligent Systems, vol. 9, no. 32, p. e2, 2022.

[37] M. Elwardy et al., "On the Consistency of 360 Video Quality Assessment in Repeated Subjective Tests:A Pilot Study," EAI Endorsed Trans. on Industrial Networks and Intell. Systems, vol. 11, no. 1, Jan. 2024.

[38] R. B. Qasem Qananwah et al., "The Utilization of EEG Signal in Video Compression," Jordanian Journalof Computers and Information Technology (JJCIT), vol. 5, no. 3, pp. 263-274, 2019.

[39] H. N. Viet et al., "COSMN: Clustering-based Optimization for 360-degree Live Streaming over MobileNetworks," EAI Endorsed Trans. on Industrial Networks and Intell. Sys., vol. 13, no. 1, pp. 1-16, 2025.

[40] X. Corbillon et al., "360-degree Video Head Movement Dataset," Proc. of the 8th ACM on MultimediaSystems Conf. (MMSys'17), pp. 199-204, DOI: 10.1145/3083187.3083215, 2017.

[41] D. Nguyen, N. V. Hung, T. T. Huong and T. C. Thang, "A Cross-layer Framework for Multi-user 360-degree Video Streaming over Cellular Networks," Proc. of the 2022 IEEE Int. Conf. on Consumer Electronics (ICCE), DOI: 10.1109/ICCE53296.2022.9730536, Las Vegas, USA, 2022.