Mención Internacional en el título de doctorThe consumption of multimedia content is moving from a residential environment to mobile
phones. Mobile data traffic, driven mostly by video demand, is increasing rapidly and wireless
spectrum is becoming a more and more scarce resource. This makes it highly important to operate
mobile networks efficiently. To tackle this, recent developments in anticipatory networking
schemes make it possible to to predict the future capacity of mobile devices and optimize the
allocation of the limited wireless resources. Further, optimizing Quality of Experience—smooth,
quick, and high quality playback—is more difficult in the mobile setting, due to the highly dynamic
nature of wireless links. A key requirement for achieving, both anticipatory networking
schemes and QoE optimization, is estimating the available bandwidth of mobile devices. Ideally,
this should be done quickly and with low overhead.
In summary, we propose a series of improvements to the delivery of multimedia over mobile
networks. We do so, be identifying inefficiencies in the interconnection of mobile operators with
the servers hosting content, propose an algorithm to opportunistically create frequent capacity estimations
suitable for use in resource optimization solutions and finally propose another algorithm
able to estimate the bandwidth class of a device based on minimal traffic in order to identify the
ideal streaming quality its connection may support before commencing playback.
The main body of this thesis proposes two lightweight algorithms designed to provide bandwidth
estimations under the high constraints of the mobile environment, such as and most notably
the usually very limited traffic quota. To do so, we begin with providing a thorough overview
of the communication path between a content server and a mobile device. We continue with
analysing how accurate smartphone measurements can be and also go in depth identifying the
various artifacts adding noise to the fidelity of on device measurements. Then, we first propose
a novel lightweight measurement technique that can be used as a basis for advanced resource
optimization algorithms to be run on mobile phones. Our main idea leverages an original packet
dispersion based technique to estimate per user capacity. This allows passive measurements by
just sampling the existing mobile traffic. Our technique is able to efficiently filter outliers introduced
by mobile network schedulers and phone hardware. In order to asses and verify our
measurement technique, we apply it to a diverse dataset generated by both extensive simulations
and a week-long measurement campaign spanning two cities in two countries, different radio
technologies, and covering all times of the day. The results demonstrate that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The
only requirement for the input data is that it should consist of a few consecutive packets that are
gathered periodically. This makes the measurement algorithm a good candidate for inclusion in
OS libraries to allow for advanced resource optimization and application-level traffic scheduling,
based on current and predicted future user capacity.
We proceed with another algorithm that takes advantage of the traffic generated by short-lived
TCP connections, which form the majority of the mobile connections, to passively estimate the
currently available bandwidth class. Our algorithm is able to extract useful information even if the
TCP connection never exits the slow start phase. To the best of our knowledge, no other solution
can operate with such constrained input. Our estimation method is able to achieve good precision
despite artifacts introduced by the slow start behavior of TCP, mobile scheduler and phone hardware.
We evaluate our solution against traces collected in 4 European countries. Furthermore, the
small footprint of our algorithm allows its deployment on resource limited devices.
Finally, in an attempt to face the rapid traffic increase, mobile application developers outsource
their cloud infrastructure deployment and content delivery to cloud computing services
and content delivery networks. Studying how these services, which we collectively denote Cloud
Service Providers (CSPs), perform over Mobile Network Operators (MNOs) is crucial to understanding
some of the performance limitations of today’s mobile apps. To that end, we perform
the first empirical study of the complex dynamics between applications, MNOs and CSPs. First,
we use real mobile app traffic traces that we gathered through a global crowdsourcing campaign
to identify the most prevalent CSPs supporting today’s mobile Internet. Then, we investigate how
well these services interconnect with major European MNOs at a topological level, and measure
their performance over European MNO networks through a month-long measurement campaign
on the MONROE mobile broadband testbed. We discover that the top 6 most prevalent CSPs
are used by 85% of apps, and observe significant differences in their performance across different
MNOs due to the nature of their services, peering relationships with MNOs, and deployment
strategies. We also find that CSP performance in MNOs is affected by inflated path length, roaming,
and presence of middleboxes, but not influenced by the choice of DNS resolver. We also
observe that the choice of operator’s Point of Presence (PoP) may inflate by at least 20% the
delay towards popular websites.This work has been supported by IMDEA Networks Institute.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Ahmed Elmokashfi.- Secretario: Rubén Cuevas Rumín.- Vocal: Paolo Din
