Search for: All records

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. 

360-degree video based virtual tours are becoming more and more popular due to travel costs and restrictions. Existing solutions leverage teleport, 3D modeling or image morphing, but none of them offers satisfactory immersion and scalability. In this paper, we propose a morphing based ultra-sparse 360-degree camera virtual tourism solution. It uses a novel bus tour mode to improve immersion; besides, it uses a series of strategies to improve feature matching such that morphing works well for ultra-sparse (15 m apart) cameras and the system can be deployed on a large scale. The experimental results show that our work results in remarkably better feature matching and synthesized views. 

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 

In recent years, streamed 360° videos have gained popularity within Virtual Reality (VR) and Augmented Reality (AR) applications. However, they are of much higher resolutions than 2D videos, causing greater bandwidth consumption when streamed. This increased bandwidth utilization puts tremendous strain on the network capacity of the cloud providers streaming these videos. In this paper, we introduce L3BOU, a novel, three-tier distributed software framework that reduces cloud-edge bandwidth in the backhaul network and lowers average end-to-end latency for 360° video streaming applications. The L3BOU framework achieves low bandwidth and low latency by leveraging edge-based, optimized upscaling techniques. L3BOU accomplishes this by utilizing down-scaled MPEG-DASH-encoded 360° video data, known as Ultra Low Resolution (ULR) data, that the L3BOU edge applies distributed super-resolution (SR) techniques on, providing a high quality video to the client. L3BOU is able to reduce the cloud-edge backhaul bandwidth by up to a factor of 24, and the optimized super-resolution multi-processing of ULR data provides a 10-fold latency decrease in super resolution upscaling at the edge. 

Sensory IoT (Internet of Things) networks are widely applied and studied in recent years and have demonstrated their unique benefits in various areas. In this paper, we bring the sensor network to an application scenario that has rarely been studied - the academic cleanrooms. We design SENSELET++, a low-cost IoT sensing platform that can collect, manage and analyze a large amount of sensory data from heterogeneous sensors. Furthermore, we design a novel hybrid anomaly detection framework which can detect both time-critical and complex non-critical anomalies. We validate SENSELET++ through the deployment of the sensing platform in a lithography cleanroom. Our results show the scalability, flexibility, and reliability properties of the system design. Also, using real-world sensory data collected by SENSELET++, our system can analyze data streams in real-time and detect shape and trend anomalies with a 91% true positive rate.