An Adaptive Video Transmission Mechanism over MEC-Based Content-Centric Networks

The rapid growth of video traffic poses serious challenges to the current Internet. Content-Centric Networking (CCN) as a promising candidate has been proposed to reengineer the Internet architecture. The in-network caching and named content communication model of CCN can enhance the video streaming applications and reduce the network workload. Due to the bandwidth-consuming characteristic of video streaming, the aggressive transmission of video data will cause a reduction of overall network efficiency. In this paper, we present an adaptive video transmission mechanism over Mobile Edge Computing- (MEC-) based CCN. The computation and storage resources of the MEC server are utilized to facilitate the video delivery. Our mechanism adopts a scalable video coding scheme to adaptively control transmission rate to cope with the network condition variation. To analyse the equilibrium property of the proposed mechanism, an analytical model is deduced by using network utility function and convex programming. We also take into account the packet loss in wired and wireless links and present a MEC assistant loss recovery algorithm. The experiment results demonstrate the performance improvement of our proposed mechanism.

[1]  Kaigui Bian,et al.  Proactive Video Push for Optimizing Bandwidth Consumption in Hybrid CDN-P2P VoD Systems , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[2]  Dario Pompili,et al.  On-Demand Video-Streaming Quality of Experience Maximization in Mobile Edge Computing , 2019, 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[3]  Jeng-Shyang Pan,et al.  An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems , 2020, Inf. Sci..

[4]  Jia Zhao,et al.  Multi-objective firefly algorithm based on compensation factor and elite learning , 2019, Future Gener. Comput. Syst..

[5]  R. Amrutha,et al.  Performance analysis of TCP incast with TCP Lite and Abstract TCP , 2015, 2015 Global Conference on Communication Technologies (GCCT).

[6]  Zhichao Zhou,et al.  Towards Adaptive Multipath Managing: A Lightweight Path Management Mechanism to Aid Multihomed Mobile Computing Devices , 2020, Applied Sciences.

[7]  George F. Riley,et al.  The ns-3 Network Simulator , 2010, Modeling and Tools for Network Simulation.

[8]  Wenfeng Wang,et al.  A Receiver-Driven Loss Recovery Mechanism for Video Dissemination over Information-Centric VANET , 2017, KSII Trans. Internet Inf. Syst..

[9]  Hui Wang,et al.  Overview of Robust Video Streaming with Network Coding , 2010, J. Inf. Hiding Multim. Signal Process..

[10]  Longzhe Han,et al.  Deep Learning based Loss Recovery Mechanism for Video Streaming over Mobile Information-Centric Network , 2019, KSII Trans. Internet Inf. Syst..

[11]  Saneyasu Yamaguchi,et al.  TCP Fairness Among Modern TCP Congestion Control Algorithms Including TCP BBR , 2018, 2018 IEEE 7th International Conference on Cloud Networking (CloudNet).

[12]  Van Jacobson,et al.  Networking named content , 2009, CoNEXT '09.

[13]  Sachin Kumar,et al.  Comparative study of TCP variants for congestion control in wireless network , 2017, 2017 International Conference on Computing, Communication and Automation (ICCCA).

[14]  N. Sreenath,et al.  Performance evaluation of TCP-Reno, TCP-Newreno and TCP-Westwood on burstification in an OBS network , 2012, 2012 18th International Conference on Advanced Computing and Communications (ADCOM).