论文标题
实时全向视频传输的边缘辅助视口自适应方案
Edge-assisted Viewport Adaptive Scheme for real-time Omnidirectional Video transmission
论文作者
论文摘要
全向应用是沉浸式和高度互动性的，可以提高工厂工人之间远程协作的效率。全向视频（OV）的传输是实施虚拟远程协作的最重要步骤。与普通的视频传输相比，OV传输需要更多的带宽，即使在5G网络下，这仍然是一个巨大的负担。基于瓷砖的方案可以减少带宽的消耗。但是，它既不准确地获得视野（FOV）区域，也不难以支持实时OV流。在本文中，我们提出了一种边缘辅助视口自适应方案（EVAS-OV），以减少实时OV传输过程中的带宽消耗。首先，EVAS-OV使用封闭式复发单元（GRU）模型来预测用户的视口。然后，将用户分为多播群集，从而进一步减少了计算资源的消耗。 EVAS-ov重新投射OV框架以准确地从像素级别获得用户的FOV区域，并采用冗余策略来减少视口预测错误的影响。将所有计算任务卸载到边缘服务器以减少传输延迟并改善带宽利用率。实验结果表明，与非观看自适应方案相比，EVAS-OV可以节省超过60 \％的带宽。与具有视口自适应的两层方案相比，EVAS-OV仍然节省了30 \％的带宽。
Omnidirectional applications are immersive and highly interactive, which can improve the efficiency of remote collaborative work among factory workers. The transmission of omnidirectional video (OV) is the most important step in implementing virtual remote collaboration. Compared with the ordinary video transmission, OV transmission requires more bandwidth, which is still a huge burden even under 5G networks. The tile-based scheme can reduce bandwidth consumption. However, it neither accurately obtain the field of view(FOV) area, nor difficult to support real-time OV streaming. In this paper, we propose an edge-assisted viewport adaptive scheme (EVAS-OV) to reduce bandwidth consumption during real-time OV transmission. First, EVAS-OV uses a Gated Recurrent Unit(GRU) model to predict users' viewport. Then, users were divided into multicast clusters thereby further reducing the consumption of computing resources. EVAS-OV reprojects OV frames to accurately obtain users' FOV area from pixel level and adopt a redundant strategy to reduce the impact of viewport prediction errors. All computing tasks were offloaded to edge servers to reduce the transmission delay and improve bandwidth utilization. Experimental results show that EVAS-OV can save more than 60\% of bandwidth compared with the non-viewport adaptive scheme. Compared to a two-layer scheme with viewport adaptive, EVAS-OV still saves 30\% of bandwidth.
