Difference between revisions of "Network and Multimedia Systems Lab (NMSL)"
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With massive investments in the virtual reality (VR) hardware sector, companies encounter the challenge of providing VR content. The current solution of installing and operating VR camera rigs is expensive and not scalable. This project provides novel algorithms and methods to automatically convert standard broadcast 2D video streams to 3D and immersive VR streams of high quality. It also provides algorithms for adaptively streaming such complex multimedia content over the Internet to heterogeneous receivers. | With massive investments in the virtual reality (VR) hardware sector, companies encounter the challenge of providing VR content. The current solution of installing and operating VR camera rigs is expensive and not scalable. This project provides novel algorithms and methods to automatically convert standard broadcast 2D video streams to 3D and immersive VR streams of high quality. It also provides algorithms for adaptively streaming such complex multimedia content over the Internet to heterogeneous receivers. | ||
| − | + | '''[[Immersive_Videos|Project Page ...]]''' | |
== '''Hyperspectral Imaging''' == | == '''Hyperspectral Imaging''' == | ||
| + | XXXX | ||
| − | + | '''[[Hyperspectral Imaging|Project Page ...]]''' | |
== '''Multiemdia Forensics''' == | == '''Multiemdia Forensics''' == | ||
| + | XXXX | ||
| + | |||
| + | '''[[Multimedia Forensics | Project Page ...]]''' | ||
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| + | == '''Cloud Gaming''' == | ||
| + | We are designing methods to improve the quality of encoded video games in cloud gaming using state-of-the-art video encoders. The goal of these methods is to optimize the quality of the encoded videos based on the content while running in realtime. | ||
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| + | '''[[Cloud Gaming | Project Page ...]]''' | ||
| + | |||
| + | * '''[[Content-aware_Video_Encoding_for_Cloud_Gaming|Content-aware Video Encoding for Cloud Gaming]]''' | ||
| + | |||
| + | * '''[[DeepGame-Efficient_Video_Encoding_for_Cloud_Gaming|DeepGame: Efficient Video Encoding for Cloud Gaming]]''' | ||
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| + | |||
| + | == '''Scalable Multicast for ISP and Datacenter Networks''' == | ||
| + | |||
| + | We are dXXX . | ||
| + | |||
| + | '''[[Scalable Multicast | Project Page ...]]''' | ||
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== '''Mobile Multimedia''' == | == '''Mobile Multimedia''' == | ||
We are designing algorithms to optimize video streaming in mobile wireless networks from different perspectives, including energy consumption of mobile receivers, quality of the videos delivered, and efficient utilization of the wireless bandwidth. | We are designing algorithms to optimize video streaming in mobile wireless networks from different perspectives, including energy consumption of mobile receivers, quality of the videos delivered, and efficient utilization of the wireless bandwidth. | ||
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| + | '''[[Mobile Multimedia | Project Page ...]]''' | ||
* '''[[hybridStreaming|Hybrid Multicast-Unicast Streaming over Mobile Networks]]''' | * '''[[hybridStreaming|Hybrid Multicast-Unicast Streaming over Mobile Networks]]''' | ||
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== '''ISP and CDN Traffic Management''' == | == '''ISP and CDN Traffic Management''' == | ||
Revision as of 10:18, 6 August 2021
Welcome to the Network and Multimedia Systems Lab (NMSL) at SFU!
We are interested in the broad areas of multimedia systems and computer networks. We develop algorithms and systems to efficiently distribute multimedia content to large-scale user communities over wired and wireless networks. We design machine learning models to address important and challenging problems in multimedia and network systems. In most of our works, we develop prototypes and testbeds to demonstrate the practicality of our solutions and show their performance in actual environments.
The Network and Multimedia Systems Lab is led by Dr. Mohamed Hefeeda. and it is located in the TASC1 building, room 8208. We hold regular group meeting for discussion and brainstorming.
Our current research projects include hyperspectral imaging, scalable multicast systems, cloud gaming, multimedia forensics, mobile multimedia, and AR/VR content processing and streaming. Brief descriptions and links to some of our active projects are given below.
Immersive and Next-Generation Videos
With massive investments in the virtual reality (VR) hardware sector, companies encounter the challenge of providing VR content. The current solution of installing and operating VR camera rigs is expensive and not scalable. This project provides novel algorithms and methods to automatically convert standard broadcast 2D video streams to 3D and immersive VR streams of high quality. It also provides algorithms for adaptively streaming such complex multimedia content over the Internet to heterogeneous receivers.
Hyperspectral Imaging
XXXX
Multiemdia Forensics
XXXX
Cloud Gaming
We are designing methods to improve the quality of encoded video games in cloud gaming using state-of-the-art video encoders. The goal of these methods is to optimize the quality of the encoded videos based on the content while running in realtime.
Scalable Multicast for ISP and Datacenter Networks
We are dXXX .
Mobile Multimedia
We are designing algorithms to optimize video streaming in mobile wireless networks from different perspectives, including energy consumption of mobile receivers, quality of the videos delivered, and efficient utilization of the wireless bandwidth.
ISP and CDN Traffic Management
We develop algorithms and systems for future ISP and CDN architectures. This includes resource management of ISP-managed CDNs (often called Telco-CDN). We develop stateless and efficient protocols and tools to support multicast traffic engineering in the ISP network.
Concluded Projects
Please check this link for some of our previous projects.
