  With the merit of containing full panoramic content in one camera, Virtual
Reality (VR) and 360-degree videos have attracted more and more attention in
the field of industrial cloud manufacturing and training. Industrial Internet
of Things (IoT), where many VR terminals needed to be online at the same time,
can hardly guarantee VR's bandwidth requirement. However, by making use of
users' quality of experience (QoE) awareness factors, including the relative
moving speed and depth difference between the viewpoint and other content,
bandwidth consumption can be reduced. In this paper, we propose OFB-VR (Optical
Flow Based VR), an interactive method of VR streaming that can make use of VR
users' QoE awareness to ease the bandwidth pressure. The Just-Noticeable
Difference through Optical Flow Estimation (JND-OFE) is explored to quantify
users' awareness of quality distortion in 360-degree videos. Accordingly, a
novel 360-degree videos QoE metric based on PSNR and JND-OFE (PSNR-OF) is
proposed. With the help of PSNR-OF, OFB-VR proposes a versatile-size tiling
scheme to lessen the tiling overhead. A Reinforcement Learning(RL) method is
implemented to make use of historical data to perform Adaptive BitRate(ABR).
For evaluation, we take two prior VR streaming schemes, Pano and Plato, as
baselines. Vast evaluations show that our system can increase the mean PSNR-OF
score by 9.5-15.8% while maintaining the same rebuffer ratio compared with Pano
and Plato in a fluctuate LTE bandwidth dataset. Evaluation results show that
OFB-VR is a promising prototype for actual interactive industrial VR. A
prototype of OFB-VR can be found in https://github.com/buptexplorers/OFB-VR.
