Quality of Experience Aware Replication Framework for Video Streaming in Content-Centric Mobile Networks Based on SDN Architecture

Author(s):  
Amna Fekih ◽  
Sonia Gaied Fantar ◽  
Habib Youssef
Author(s):  
Edma V.C. Urtiga Mattos ◽  
Gustavo M. Torres ◽  
Mateus O. Da Silva ◽  
Victor L.G. Calvacante ◽  
Adriel V. Dos Santos ◽  
...  

Author(s):  
Árpád Huszák

In this chapter we present a novel selective retransmission scheme, based on congestion control algorithm. Our method is efficient in narrowband networks for multimedia applications, which demand higher bandwidth. Multimedia applications are becoming increasingly popular in IP networks, while in mobile networks the limited bandwidth and the higher error rate arise in spite of its popularity. These are restraining factors for mobile clients using multimedia applications such as video streaming. In some conditions the retransmission of lost and corrupted packets should increase the quality of the multimedia service, but these retransmissions should be enabled only if the network is not in congested state. Otherwise the retransmitted packet will intensify the congestion and it will have negative effect on the audio/video quality. Our proposed mechanism selectively retransmits the corrupted packets based on the actual video bit rate and the TCP-Friendly Rate Control (TFRC), which is integrated to the preferred DCCP transport protocol.


2020 ◽  
Vol 17 (4) ◽  
pp. 2702-2716
Author(s):  
Simone Porcu ◽  
Alessandro Floris ◽  
Jan-Niklas Voigt-Antons ◽  
Luigi Atzori ◽  
Sebastian Moller

2019 ◽  
Vol 9 (11) ◽  
pp. 2297
Author(s):  
Kyeongseon Kim ◽  
Dohyun Kwon ◽  
Joongheon Kim ◽  
Aziz Mohaisen

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.


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