University of Cambridge > Talks.cam > Computer Laboratory Systems Research Group Seminar > Practical Edge Computing for Mobile X Reality

Practical Edge Computing for Mobile X Reality

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Smartphone is now a necessity of people’s daily life, and we are enjoying various services on it with numerous mobile applications. However, the resource and communication limitations of a single mobile device make it insufficient in satisfying the real-time and interactive constraints of some computation intensive applications, such as mobile Augmented Reality (AR) and mobile Virtual Reality (VR). To bridge the gap, we utilize the processing power of edge servers via task offloading and build practical mobile systems which significantly outperforms state-of-the-art mobile systems in terms of latency, scalability, quality of experience (QoE), and many other aspects.

In the case of mobile Augmented Reality, large-scale object recognition is an essential but time-consuming task. To offload the object recognition task and enhance the system performance, we explore how the GPU and the multi-core architecture on the edge servers would accelerate the large-scale object recognition process. With the carefully designed offloading pipeline and edge acceleration, we are able to finish the whole AR pipeline within one camera frame interval while maintaining high recognition accuracy with large-scale datasets. In the case of mobile Virtual Reality, existing 360 degree video streaming systems are suffering from insufficient pixel density, as the video resolution falling within the user’s field of view (FoV) is relatively low. We utilize the edge server for ultra-high resolution video transcoding and implement a system which streams tile-based viewport adaptive 360 degree videos onto the mobile client. With this edge proxy, we successfully achieve 16K 360 degree video streaming onto off-the-shelf smartphones, achieving high frame quality and fluent playback without overwhelming the processing capacity of the smartphones.

Short Biography:

Pan Hui received his PhD from Computer Laboratory, University of Cambridge under the supervision of Jon Crowcroft, and both his Bachelor and MPhil degrees from the University of Hong Kong. He is the Nokia Chair in Data Science, elected by Nokia Bell Labs, at the University of Helsinki and the director of the HKUST -DT System and Media Lab at the Hong Kong University of Science and Technology. He was a senior research scientist and then a Distinguished Scientist for Telekom Innovation Laboratories (T-labs) Germany. He is an International Fellow of the Royal Academy of Engineering, a Fellow of the Institute of Electrical and Electronics Engineers, a Member of the Academia Europaea, and a Distinguished Scientist of the Association for Computing Machinery.

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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