KEYWORDS: Video acceleration, Video, 3D video streaming, Clouds, Web services, Computer architecture, Video processing, Mobile devices, 3D applications, Virtual reality, Computer architecture, Visualization, Neodymium, Graphics processing units, Operating systems
This paper proposes a new approach to improving the application of 3D video rendering and streaming by jointly exploring and optimizing both cloud-based virtualization and web-based delivery. The proposed web service architecture firstly establishes a software virtualization layer based on QEMU (Quick Emulator), an open-source virtualization software that has been able to virtualize system components except for 3D rendering, which is still in its infancy. The architecture then explores the cloud environment to boost the speed of the rendering at the QEMU software virtualization layer. The capabilities and inherent limitations of Virgil 3D, which is one of the most advanced 3D virtual Graphics Processing Unit (GPU) available, are analyzed through benchmarking experiments and integrated into the architecture to further speed up the rendering. Experimental results are reported and analyzed to demonstrate the benefits of the proposed approach.
KEYWORDS: Video, Clouds, Mobile devices, Video compression, Video processing, Video coding, Computer programming, Video surveillance, Internet, Temporal resolution
The recent explosion in video-related Internet traffic has been driven by the widespread use of smart mobile devices,
particularly smartphones with advanced cameras that are able to record high-quality videos. Although many of these
devices offer the facility to record videos at different spatial and temporal resolutions, primarily with local storage
considerations in mind, most users only ever use the highest quality settings. The vast majority of these devices are
optimised for compressing the acquired video using a single built-in codec and have neither the computational resources
nor battery reserves to transcode the video to alternative formats. This paper proposes a new low-complexity dynamic
resource allocation engine for cloud-based video transcoding services that are both scalable and capable of being
delivered in real-time. Firstly, through extensive experimentation, we establish resource requirement benchmarks for a
wide range of transcoding tasks. The set of tasks investigated covers the most widely used input formats (encoder type,
resolution, amount of motion and frame rate) associated with mobile devices and the most popular output formats
derived from a comprehensive set of use cases, e.g. a mobile news reporter directly transmitting videos to the TV
audience of various video format requirements, with minimal usage of resources both at the reporter’s end and at the
cloud infrastructure end for transcoding services.
KEYWORDS: Video, Visualization, Video acceleration, Clouds, Video processing, OpenGL, 3D video streaming, Video coding, 3D displays, Computer programming
This paper describes a comprehensive empirical performance evaluation of 3D video processing employing the physical/virtual architecture implemented in a cloud environment. Different virtualization technologies, virtual video cards and various 3D benchmarks tools have been utilized in order to analyse the optimal performance in the context of 3D online gaming applications. This study highlights 3D video rendering performance under each type of hypervisors, and other factors including network I/O, disk I/O and memory usage. Comparisons of these factors under well-known virtual display technologies such as VNC, Spice and Virtual 3D adaptors reveal the strengths and weaknesses of the various hypervisors with respect to 3D video rendering and streaming.
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