An unprecedented era of “connected vehicles” is becoming an imminent reality. This
is driven by advances in vehicular communications, and the development of in-vehicle
telematics systems supporting a plethora of applications. The diversity and multitude
of such developments will, however, introduce excessive congestion across wireless
infrastructure, compelling operators to expand their networks. An alternative to
network expansions is to develop more efficient content delivery paradigms. In particular,
alleviating Radio Access Network (RAN) congestion is important to operators
as it postpones costly investments in radio equipment installations and new spectrum.
Efficient RAN frameworks are therefore paramount to expediting this realm
of vehicular connectivity.
Fortunately, the predictability of human mobility patterns, particularly that of vehicles
traversing road networks, offers unique opportunities to pursue proactive RAN
transmission schemes. Knowing the routes vehicles are going to traverse enables the
network to forecast spatio-temporal demands and predict service outages that specific
users may face. This can be accomplished by coupling the mobility trajectories with
network coverage maps to provide estimates of the future rates users will encounter
along a trip.
In this thesis, we investigate how this valuable contextual information can enable RANs to improve both service quality and operational efficiency. We develop a collection
of methods that leverage mobility predictions to jointly optimize 1) long-term
wireless resource allocation, 2) adaptive video streaming delivery, and 3) energy efficiency in RANs. Extensive simulation results indicate that our approaches provide
significant user experience gains in addition to large energy savings. We emphasize
the applicability of such predictive RAN mechanisms to video streaming delivery, as
it is the predominant source of traffic in mobile networks, with projections of further
growth. Although we focus on exploiting mobility information at the radio access
level, our framework is a direction towards pursuing a predictive end-to-end content
delivery architecture. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2014-04-30 06:15:34.31
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/12162 |
| Date | 01 May 2014 |
| Creators | Abou-zeid, Hatem |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
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