scholarly journals An end-to-end approach for optimal mode selection in Internet video communication: theory and application

2000 ◽  
Vol 18 (6) ◽  
pp. 977-995 ◽  
Author(s):  
D. Wu ◽  
Y.T. Hou ◽  
B. Li ◽  
Wenwu Zhu ◽  
Ya-Qin Zhang ◽  
...  
Keyword(s):  
Communication Theory ◽  
Mode Selection ◽  
Video Communication ◽  
Internet Video ◽  
Optimal Mode ◽  
End To End

scholarly journals Optimal Mode Selection for Full-Duplex Enabled D2D Cognitive Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 57298-57311 ◽  
Author(s):  
Noman Haider ◽  
Ahsan Ali ◽  
Cristo Suarez-Rodriguez ◽  
Eryk Dutkiewicz
Keyword(s):  
Mode Selection ◽  
Cognitive Networks ◽  
Full Duplex ◽  
Optimal Mode ◽  
Selection For

scholarly journals Optimal Mode Selection for a Pulsed-Quality Dual-Frame Video Coder

2004 ◽  
Vol 11 (12) ◽  
pp. 952-955 ◽  
Author(s):  
A. Leontaris ◽  
V. Chellappa ◽  
P.C. Cosman
Keyword(s):  
Mode Selection ◽  
Dual Frame ◽  
Video Coder ◽  
Optimal Mode ◽  
Selection For

Quality adaptive end-to-end packet scheduling to avoid playout interruptions in Internet video streaming systems

2010 ◽  
Vol 83 (8) ◽  
pp. 1489-1499 ◽  
Author(s):  
Rossella Fortuna ◽  
Luigi Alfredo Grieco ◽  
Gennaro Boggia ◽  
Pietro Camarda
Keyword(s):  
Video Streaming ◽  
Packet Scheduling ◽  
Internet Video ◽  
End To End ◽  
Streaming Systems

scholarly journals Improved D2D Millimeter Wave Communications for 5G Networks Using Deep Learning.pdf

2020 ◽  
Author(s):  
Ahmed abdelreheem ◽  
Ahmed S. A. Mubarak ◽  
Osama A. Omer ◽  
Hamada Esmaiel ◽  
Usama S. Mohamed
Keyword(s):  
Deep Learning ◽  
Millimeter Wave ◽  
Mode Selection ◽  
Low Complexity ◽  
5G Networks ◽  
Coverage Area ◽  
High Efficient ◽  
Optimal Mode ◽  
Low Coverage ◽  
Deep Learning Model

Mode selection is normally used in conjunction with Device-to-Device (D2D) millimeter wave (mmWave) communications in 5G networks to overcome the low coverage area, poor reliability and vulnerable to path blocking of mmWave transmissions. Thus, producing a high-efficient D2D mmWave using mode selection based on select the optimal mode with low complexity turns to be a big challenge towards ubiquitous D2D mmWave communications. In this paper, low complexity and high-efficient mode selection in D2D mmWave communications based on deep learning is introduced utilizing the artificial intelligence. In which, deep learning is used to estimate the optimal mode y in the case of blocking of mmWave transmission or low coverage area of mmWave communications. Then, the proposed deep learning model is based on training the model with almost use cases in offline phase to predict the optimal mode for data relaying high-reliability communication in online phase. In mode selection process, the potential D2D transmitter select the mode to transmit the data either based on dedicated D2D communication or through the cellular uplink using the base station (BS) as a relay based on several criteria. The proposed deep learning model is developed to overcome the challenges of selected the optimal mode with low complexity and high efficiency. The simulation analysis show that the proposed mode selection algorithms outperform the conventional techniques in D2D mmWave communication in the spectral efficiency, energy efficiency and coverage probability.

scholarly journals An optimal mode selection algorithm for scalable video coding

2020 ◽  
Vol 10 (2) ◽  
pp. 143 ◽  
Author(s):  
L. Balaji ◽  
K.K. Thyagharajan ◽  
C. Raja ◽  
A. Dhanalakshmi
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Mode Selection ◽  
Scalable Video ◽  
Selection Algorithm ◽  
Optimal Mode
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