Skip to main content Accessibility help
×
Home
Hostname: page-component-684899dbb8-ct24h Total loading time: 0.419 Render date: 2022-05-22T02:30:25.198Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

4 - Cooperative Caching in Cloud-Assisted 5G Wireless Networks

from Part I - Optimal Cache Placement and Delivery

Published online by Cambridge University Press:  19 October 2020

Thang X. Vu
Affiliation:
Université du Luxembourg
Ejder Baştuğ
Affiliation:
Nokia Bell Labs, France
Symeon Chatzinotas
Affiliation:
Université du Luxembourg
Tony Q. S. Quek
Affiliation:
Singapore University of Technology and Design
Get access

Summary

Cloud-assisted wireless networks are emerging solutions that unite wireless networks and cloud computing to deliver cloud services directly from the network edges to support the foreseen massive demands from data- and computation-hungry mobile users. In this chapter, we first provide an overview of the two emerging cloud-assisted wireless network paradigms – namely, the cloud radio access network (C-RAN), which aims at centralization of base station (BS) functionalities, and mobile-edge computing (MEC), which aims at providing the RAN with computing and storage resources. We then leverage the C-RAN and MEC paradigms to design novel cooperative caching frameworks that explore the synergies of the in-network computing and storage resources. Specifically, a novel cooperative hierarchical caching framework is designed in C-RAN, where caching is performed both at the distributed BSs and at the cloud processing unit (CPU), which bridges the gap between the traditional edge-based and core-based caching schemes. Furthermore, a joint cooperative caching and processing framework is designed in a MEC network, where the MEC servers perform both cache storage and video transcoding to support adaptive bitrate (ABR) video streaming.

Type
Chapter
Information
Wireless Edge Caching
Modeling, Analysis, and Optimization
, pp. 66 - 88
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×