Interactive Virtual Reality (VR) streaming over Wi-Fi networks encounters significant challenges due to bandwidth fluctuations caused by channel contention and user mobility. Adaptive BitRate (ABR) algorithms dynamically adjust the video encoding bitrate based on the available network capacity, aiming to maximize image quality while mitigating congestion and preserving the user's Quality of Experience (QoE). In this paper, we experiment with ABR algorithms for VR streaming using Air Light VR (ALVR), an open-source VR streaming solution. We extend ALVR with a comprehensive set of metrics that provide a robust characterization of the network's state, enabling more informed bitrate adjustments. To demonstrate the utility of these performance indicators, we develop and test the Network-aware Step-wise ABR algorithm for VR streaming (NeSt-VR). Results validate the accuracy of the newly implemented network performance metrics and demonstrate NeSt-VR's video bitrate adaptation capabilities.

翻译：在Wi-Fi网络上进行交互式虚拟现实（VR）流媒体传输，由于信道竞争和用户移动性导致的带宽波动而面临重大挑战。自适应比特率（ABR）算法根据可用网络容量动态调整视频编码比特率，旨在最大化图像质量，同时缓解拥塞并保持用户的服务质量（QoE）。本文中，我们使用开源VR流媒体解决方案Air Light VR（ALVR）对VR流媒体的ABR算法进行了实验。我们扩展了ALVR，引入了一套全面的度量指标，这些指标能够稳健地表征网络状态，从而实现更明智的比特率调整。为了展示这些性能指标的实用性，我们开发并测试了用于VR流媒体的网络感知步进式ABR算法（NeSt-VR）。实验结果验证了新实现的网络性能指标的准确性，并展示了NeSt-VR的视频比特率自适应能力。