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→Multimedia Resource Allocation for QoE Improvement by Deep Learning
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=== Multimedia Resource Allocation for QoE Improvement by Deep Learning=== | === Multimedia Resource Allocation for QoE Improvement by Deep Learning=== | ||
* '''New!''' Deep learning has been widely used in various real-time applications and systems. Dynamic resource allocation for multimedia (e.g. Video) to improve QoE is an interesting topic. We need | * '''New!''' Deep learning has been widely used in various real-time applications and systems. Dynamic resource allocation for multimedia (e.g. Video) to improve QoE is an interesting topic. We need three students for this topic. We expect you have a background in deep learning and computer network, as well as programming skills like Python and Go. | ||
(1) one to realize and improve the system for video transmission and network configuration according to resource allocation policy; | (1) one to realize and improve the system for video transmission and network configuration according to resource allocation policy; | ||
* You will use QUIC [https://github.com/lucas-clemente/quic-go] protocol (Go language) to implement network allocation and place the server part on AWS/other clouds. | * You will use QUIC [https://github.com/lucas-clemente/quic-go] protocol (Go language) to implement network allocation and place the server part on AWS/other clouds. | ||
(2) one to implement the deep learning algorithm to design the controller for dynamic resource allocations. | |||
(2) | (3) one student for the QoE model using deep learning. | ||
Please contact Dr.Tingting Yuan [tingting.yuan@cs.uni-goettingen.de ] and Weijun Wang [weijun.wang@informatik.uni-goettingen.de](B/M/P) | Please contact Dr.Tingting Yuan [tingting.yuan@cs.uni-goettingen.de ] and Weijun Wang [weijun.wang@informatik.uni-goettingen.de](B/M/P) | ||
=== Low Power, Wide Area (LPWA) technologies on smart cities=== | === Low Power, Wide Area (LPWA) technologies on smart cities=== | ||