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360-degree video is becoming an integral part of our content consumption through both video on demand and live broadcast services. However, live broadcast is still challenging due to the huge network bandwidth cost if all 360-degree views are delivered to a large viewer population over diverse networks. In this paper, we present 360BroadView, a viewer management approach to viewport prediction in 360-degree video live broadcast. We make some highbandwidth network viewers be leading viewers to help the others (lagging viewers) predict viewports during 360-degree video viewing and save bandwidth. Our viewer management maintains the leading viewer population despite viewer churns during live broadcast, so that the system keeps functioning properly. Our evaluation shows that 360BroadView maintains the leading viewer population at a minimal yet necessary level for 97 percent of the time. 

Immersive virtual tours based on 360-degree cameras, showing famous outdoor scenery, are becoming more and more desirable due to travel costs, pandemics and other constraints. To feel immersive, a user must receive the view accurately corresponding to her position and orientation in the virtual space when she moves inside, and this requires cameras’ orientations to be known. Outdoor tour contexts have numerous, ultra-sparse cameras deployed across a wide area, making camera pose estimation challenging. As a result, pose estimation techniques like SLAM, which require mobile or dense cameras, are not applicable. In this paper we present a novel strategy called 360ViewPET, which automatically estimates the relative poses of two stationary, ultra-sparse (15 meters apart) 360-degree cameras using one equirectangular image taken by each camera. Our experiments show that it achieves accurate pose estimation, with a mean error as low as 0.9 degree