# Pano: Optimizing 360deg Video Streaming with a Better Understanding of Quality Perception

arXiv: https://arxiv.org/abs/1911.04139 · HTML: https://mlsanity.com/paper/1911.04139/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [NetLLM: Adapting Large Language Models for Networking](https://mlsanity.com/paper/2402.02338/) (2024)
- [CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale](https://mlsanity.com/paper/2105.06524/) (2021)
- [Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality](https://mlsanity.com/paper/2212.09945/) (2022)
- [A review on Machine Learning based User-Centric Multimedia Streaming Techniques](https://mlsanity.com/paper/2411.15801/) (2024)
- [Applying VertexShuffle Toward 360-Degree Video Super-Resolution on Focused-Icosahedral-Mesh](https://mlsanity.com/paper/2106.11253/) (2021)
- [OmniSense: Towards Edge-Assisted Online Analytics for 360-Degree Videos](https://mlsanity.com/paper/2508.14237/) (2025)
- [Artic: AI-oriented Real-time Communication for MLLM Video Assistant](https://mlsanity.com/paper/2602.12641/) (2026)
- [Training-Free Adaptive 360-degree Video Streaming via Semantic Potential Fields](https://mlsanity.com/paper/2603.20999/) (2026)

## Abstract (arXiv)

Streaming 360deg videos requires more bandwidth than non-360deg videos. This is because current solutions assume that users perceive the quality of 360deg videos in the same way they perceive the quality of non-360deg videos. This means the bandwidth demand must be proportional to the size of the user's field of view. However, we found several qualitydetermining factors unique to 360degvideos, which can help reduce the bandwidth demand. They include the moving speed of a user's viewpoint (center of the user's field of view), the recent change of video luminance, and the difference in depth-of-fields of visual objects around the viewpoint. This paper presents Pano, a 360deg video streaming system that leverages the 360deg video-specific factors. We make three contributions. (1) We build a new quality model for 360deg videos that captures the impact of the 360deg video-specific factors. (2) Pano proposes a variable-sized tiling scheme in order to strike a balance between the perceived quality and video encoding efficiency. (3) Pano proposes a new qualityadaptation logic that maximizes 360deg video user-perceived quality and is readily deployable. Our evaluation (based on user study and trace analysis) shows that compared with state-of-the-art techniques, Pano can save 41-46% bandwidth without any drop in the perceived quality, or it can raise the perceived quality (user rating) by 25%-142% without using more bandwidth.