While traditional multimedia applications such as games and videos are still popular, there has been a significant interest in the recent years towards new 3D media such as 3D immersion and Virtual Reality (VR) applications, especially 360 VR videos. 360 VR video is an immersive spherical video where the user can look around during playback. Unfortunately, 360 VR videos are extremely bandwidth intensive, and therefore are difficult to stream at acceptable quality levels.In this paper, we propose an adaptive bandwidth-efficient 360 VR video streaming system using a divide and conquer approach. We propose a dynamic view-aware adaptation technique to tackle the huge bandwidth demands of 360 VR video streaming. We spatially divide the videos into multiple tiles while encoding and packaging, use MPEG-DASH SRD to describe the spatial relationship of tiles in the 360-degree space, and prioritize the tiles in the Field of View (FoV). In order to describe such tiled representations, we extend MPEG-DASH SRD to the 3D space of 360 VR videos. We spatially partition the underlying 3D mesh, and construct an efficient 3D geometry mesh called hexaface sphere to optimally represent a tiled 360 VR video in the 3D space. Our initial evaluation results report up to 72% bandwidth savings on 360 VR video streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.

Virtual Reality (VR) devices are becoming accessible to a large public, which is going to increase the demand for 360°VR videos. VR videos are often characterized by a poor quality of experience, due to the high bandwidth required to stream the 360°video. To overcome this issue, we spatially divide the VR video into tiles, so that each temporal segment is composed of several spatial tiles. Only the tiles belonging to the viewport, the region of the video watched by the user, are streamed at the highest quality. The other tiles are instead streamed at a lower quality. We also propose an algorithm to predict the future viewport position and minimize quality transitions during viewport changes. The video is delivered using the server push feature of the HTTP/2 protocol. Instead of retrieving each tile individually, the client issues a single push request to the server, so that all the required tiles are automatically pushed back to back. This approach allows to increase the achieved throughput, especially in mobile, high RTT networks. In this paper, we detail the proposed framework and present a prototype developed to test its performance using real-world 4G bandwidth traces. Particularly, our approach can save bandwidth up to 35% without severely impacting the quality viewed by the user, when compared to a traditional non-tiled VR streaming solution. Moreover, in high RTT conditions, our HTTP/2 approach can reach 3 times the throughput of tiled streaming over HTTP/1.1, and consistently reduce freeze time. These results represent a major improvement for the e cient delivery of 360°VR videos over the Internet.

High-quality point clouds have recently gained interest as an emerging form of representing immersive 3D graphics. Unfortunately, these 3D media are bulky and severely bandwidth intensive, which makes it difficult for streaming to resourcelimited and mobile devices. This has called researchers to propose efficient and adaptive approaches for streaming of high-quality point clouds.In this paper, we run a pilot study towards dynamic adaptive point cloud streaming, and extend the concept of dynamic adaptive streaming over HTTP (DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of dense point cloud streaming while at the same time can semantically link to human visual acuity to maintain high visual quality when needed. In order to describe the various quality representations, we propose multiple thinning approaches to spatially sub-sample point clouds in the 3D space, and design a DASH Media Presentation Description manifest specific for point cloud streaming. Our initial evaluations show that we can achieve significant bandwidth and performance improvement on dense point cloud streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied. CCS CONCEPTS•Information systems →Multimedia streaming; ACM Reference format:

The era of big data has led to the emergence of new systems for real-time distributed stream processing, e.g., Apache Storm is one of the most popular stream processing systems in industry today. However, Storm, like many other stream processing systems lacks an intelligent scheduling mechanism. The default round-robin scheduling currently deployed in Storm disregards resource demands and availability, and can therefore be inefficient at times. We present R-Storm (Resource-Aware Storm), a system that implements resourceaware scheduling within Storm. R-Storm is designed to increase overall throughput by maximizing resource utilization while minimizing network latency. When scheduling tasks, R-Storm can satisfy both soft and hard resource constraints as well as minimizing network distance between components that communicate with each other. We evaluate R-Storm on set of micro-benchmark Storm applications as well as Storm applications used in production at Yahoo! Inc. From our experimental results we conclude that R-Storm achieves 30-47% higher throughput and 69-350% better CPU utilization than default Storm for the micro-benchmarks. For the Yahoo! Storm applications, R-Storm outperforms default Storm by around 50% based on overall throughput. We also demonstrate that R-Storm performs much better when scheduling multiple Storm applications than default Storm.

3D tele-immersion improves the state of collaboration among geographically distributed participants. Unlike the traditional 2D videos, a 3D tele-immersive system employs multiple 3D cameras based in each physical site to cover a much larger field of view, generating a very large amount of stream data. One of the major challenges is how to efficiently transmit these bulky 3D streaming data to bandwidth-constrained sites. In this paper, we study an adaptive Human Visual System (HVS) -compliant bandwidth management framework for efficient delivery of hundred-scale streams produced from distributed 3D tele-immersive sites to a receiver site with limited bandwidth budget. Our adaptation framework exploits the semantics link of HVS with multiple 3D streams in the 3D tele-immersive environment. We developed TELEVIS, a visual simulation tool to showcase a HVS-aware tele-immersive system for realistic cases. Our evaluation results show that the proposed adaptation can improve the total quality per unit of bandwidth used to deliver streams in 3D teleimmersive systems.

Advances in computing hardware and multimedia applications have spurred the development of mobile devices such as smartphones and PDAs. Amongst the most used applications on handheld devices are mobile 3D graphics such as 3D games and virtual environments. With this significant increase of mobile applications, one of the challenges is how to efficiently transmit the bulky 3D information to resource-constrained mobile devices. They impose significant demands on the limited battery capacity of mobile devices. Thus deploying efficient approaches to decrease the amount of streamed data with the aim of increasing the battery lifetime has become a key research topic.In this study, we design and implement an adaptive priority-based framework for efficiently streaming 3D textures to mobile devices with limited energy budget over wireless networks. Our results show that using our proposed adaptations significantly improves the gameplay quality per unit of energy consumed to download 3D textures in mobile games.

This study aimed to assess the awareness and performance of Qom citizens towards using disinfectants and compared its relationship with geographical distribution of COVID-19 outbreak in Qom, Iran. The study was conducted by a researcher-made questionnaire during April and May, 2020. COVID-19 incidence data for each district of city was obtained from health department of Qom province. Data were analyzed using Excel, SPSS and ArcView (GIS) softwares. It was found that the highest level of citizens’ awareness (52%) was in the weak range while their performance (56%) was in the good range. According to Spearman’s correlation analysis, there was a strong correlation (rho 0.95) between the total mean of awareness and performance (p < 0.01). The highest incidence rate of COVID-19 was in district 7 which had the lowest mean score in both awareness and performance. In addition, the results of ANOVA (LSD—least significant difference) showed that there was a significant difference (p < 0.05) between district 7—with lower mean scores in awareness and performance—and other districts. Overall, it is concluded that citizens’ awareness level was lower than that of their performance. This conclusion not only calls for more training programs to be implemented in public places, schools, universities and governmental offices, but it also necessitates maintaining a proper and timely training about using disinfectants.

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