Hybrid-360: An adaptive bitrate algorithm for tile-based 360 video streaming

In recent years, virtual reality (VR) has become a popular topic. VR videos, as one of the popular applications, can provide the user with an extraordinary and immersed video viewing experience and are gradually entering the public's vision. However, it has faced specific difficulties in the past few years. VR videos need specific collection and displaying equipment. Delivering VR videos needs extra bandwidth and storage space as they contain more information. Traditional adaptive bitrate (ABR) algorithms are mainly designed for 2D videos, and cannot directly apply to VR videos. The delay must be neglectable and there should be fewer stalls. If not, users can suffer from motion sickness. With the development of relevant technologies, those problems can be solved. Using a first-class VR camera and a head-mounted display, VR videos can be easily collected and displayed properly. In this work, Hybrid-360, a novel tile-based 360 video ABR algorithm, is proposed. Using reinforcement learning, the algorithm can make proper bitrate decisions for every tile in the VR video based on the network and client status. In addition, a system model is proposed to implement the tile-based 360 ABR algorithm in the real world. Different from traditional 2D videos, VR videos have special characteristics, which brings the need to redesign quality of experience (QoE) model. Additional QoE factors are added into the QoE model, and the weights of every term are adjusted. Using a novel 360 video simulation platform, Hybrid-360 is then compared with some classical ABR algorithms. It is found to be capable of balancing the video quality as well as video smoothness and fluentness. Also, the algorithm can provide users with a higher QoE.