A Network Paradigm for Very High Capacity Mobile and Fixed Telecommunications Ecosystem Sustainable Evolution

The main objective for Very High Capacity (VHC) fixed and mobile networks is improving end-user Quality of Experience (QoE), i.e., meeting the Key Performance Indicators (KPIs) – throughput, download time, round trip time, and video delay – required by the applications. KPIs depend on the end-to-end connection between the server and the end-user device. Not only Telco operators must provide the quality needed for the different applications, but also they must address economic sustainability objectives for VHC networks. Today, both goals are often not met, mainly due to the push to increase the access networks bit-rate without considering the end-to-end applications KPIs. This paper’s main contribution deals with the definition of a VHC network deployment framework able to address performance and cost issues. We show that three are the interventions on which it is necessary to focus: $i$ ) the reduction of bit-rate through video compression, ii) the reduction of packet loss rate through artificial intelligence algorithms for access lines stabilization, and iii) the reduction of latency (i.e., the round-trip time) with edge-cloud computing and content delivery platforms, including transparent caching. The concerted and properly phased action of these three measures can allow a Telco to get out of the Ultra Broad Band access network “trap” as defined in the paper. We propose to work on the end-to-end optimization of the bandwidth utilization ratio (i.e., the ratio between the throughput and the bit-rate that any application can use). It leads to better performance experienced by the end-user, enables new business models and revenue streams, and provides a sustainable cost for the Telco operators. To make such a perspective more precise, the case of MoVAR (Mobile Virtual and Augmented Reality), one of the most challenging future services, is finally described.

[1]  Francesco Vatalaro,et al.  Content Delivery on IP Network: Service Providers and TV Broadcasters Business Repositioning , 2019, 2019 3rd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom).

[2]  P. Krishnan,et al.  The cache location problem , 2000, TNET.

[3]  Van Jacobson,et al.  BBR: Congestion-Based Congestion Control , 2016, ACM Queue.

[4]  Nick Feamster,et al.  Internet Speed Measurement: Current Challenges and Future Recommendations , 2019, ArXiv.

[5]  Li Fan,et al.  Web caching and Zipf-like distributions: evidence and implications , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[6]  F. Vatalaro,et al.  Edge Cloud Computing in Telecommunications: Case Studies on Performance Improvement and TCO Saving , 2019, 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC).

[7]  Phuoc Tran-Gia,et al.  A Survey on Quality of Experience of HTTP Adaptive Streaming , 2015, IEEE Communications Surveys & Tutorials.

[8]  Vincenzo Sciancalepore,et al.  From network sharing to multi-tenancy: The 5G network slice broker , 2016, IEEE Communications Magazine.

[9]  Daniele Roffinella,et al.  Performance improvement and network TCO reduction by optimal deployment of caching , 2014, 2014 Euro Med Telco Conference (EMTC).

[10]  Xin Zhang,et al.  Edgecourier: an edge-hosted personal service for low-bandwidth document synchronization in mobile cloud storage services , 2017, SEC.

[11]  Matthew Mathis,et al.  The macroscopic behavior of the TCP congestion avoidance algorithm , 1997, CCRV.

[12]  Feng Qian,et al.  An in-depth study of LTE: effect of network protocol and application behavior on performance , 2013, SIGCOMM.

[13]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.