Over the past decades, 360-degree videos have attracted wide interest for the immersive experience they bring to viewers. The rising of high-resolution 360-degree videos greatly challenges the traditional video streaming systems in limited network environments. Given the limited bandwidth, tile-based video streaming with adaptive bitrate selection has been widely studied to improve the Quality of Experience (QoE) of viewers by tiling the video frames and allocating different bitrates for tiles inside and outside viewers' viewports. Existing solutions for viewport prediction and bitrate selection train general models without catering to the intrinsic need for personalization. In this paper, we present the first meta-learning-based personalized 360-degree video streaming framework. The commonality among viewers of different viewing patterns and QoE preferences is captured by efficient meta-network designs. Specifically, we design a meta-based long-short term memory model for viewport prediction and a meta-based reinforcement learning model for bitrate selection. Extensive experiments on real-world datasets demonstrate that our framework not only outperforms the state-of-the-art data-driven approaches in prediction accuracy by 11% on average and improves QoE by 27% on average, but also quickly adapts to users with new preferences with on average 67%-88% less training epochs.