# Power-Efficient Wireless Streaming of Multi-Quality Tiled 360 VR Video in MIMO-OFDMA Systems

arXiv: https://arxiv.org/abs/2105.09865 · HTML: https://mlsanity.com/paper/2105.09865/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning](https://mlsanity.com/paper/2107.01001/) (2021)
- [WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery](https://mlsanity.com/paper/2211.01241/) (2022)
- [Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback](https://mlsanity.com/paper/2201.12525/) (2022)
- [Balancing Latency and Model Accuracy for Fluid Antenna-Assisted LM-Embedded MIMO Network](https://mlsanity.com/paper/2508.01689/) (2025)

## Abstract (arXiv)

In this paper, we study the optimal wireless streaming of a multi-quality tiled 360 virtual reality (VR) video from a multi-antenna server to multiple single-antenna users in a multiple-input multiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA) system. In the scenario without user transcoding, we jointly optimize beamforming and subcarrier, transmission power, and rate allocation to minimize the total transmission power. This problem is a challenging mixed discretecontinuous optimization problem. We obtain a globally optimal solution for small multicast groups, an asymptotically optimal solution for a large antenna array, and a suboptimal solution for the general case. In the scenario with user transcoding, we jointly optimize the quality level selection, beamforming, and subcarrier, transmission power, and rate allocation to minimize the weighted sum of the average total transmission power and the transcoding power. This problem is a two-timescale mixed discrete-continuous optimization problem, which is even more challenging than the problem for the scenario without user transcoding. We obtain a globally optimal solution for small multicast groups, an asymptotically optimal solution for a large antenna array, and a low-complexity suboptimal solution for the general case. Finally, numerical results demonstrate the significant gains of proposed solutions over the existing solutions. significant gains of proposed solutions over the existing solutions.