| dc.description.abstract | Quality of Experience (QoE) is a prototype of human feelings, wishes, impressions and
motives. Quality of Service (QoS) on the other hand is a technology-based metric that is
employed to gauge how a network performs at any given time. Latterly, QoE in Next
Generation Mobile Networks ( GMNs) - mostly referred to as 4G Long Term Evolution (LTE)
networks have continued to attract interest from network researchers, developers and
designers. This is mainly because QoE on its own comes with a degree of relationship to QoS
or network conditions, yet to end users QoE matters much more than QoS. QoS is basically
about network metrics such as jitter, pack delay and loss. Much as these metrics are critical
too, they are only a small part of satisfying users, this is because QoE is more related to the
perception of a service by a mobile user.
The main challenge that remains is; mobile users always expect their mobile devices to have
full connectivity and high network performance, if any of this is absent, providers of this
service do not have sufficient time to wait for these quality complaints from the users to come
through; instead such users will simply change service providers in the hope of finding a better
service on another network. This confirms that excellent network metrics can easily mean
nothing especially if users decide to find other service providers. This occurrence pauses two
challenges; first, mobile users will continue to look for better quality video content streaming
without breakouts elsewhere, secondly the networks they may keep moving to are bound to
experience a knock-on effect as far as network resource utilization is concerned, this is mainly
because of a sudden increase in the number of users accessing the same service at the time. In
this same period, video streaming also continues to have a significant influence on network
traffic especially in NGMNs. The major force behind this influence has been satisfaction of
user experience as far as service delivery is concerned.
Therefore, it is important that service providers hold a means of continuity in the measurement
of the QoE since QoE may not only be judged on network speed improvement such as
deploying advanced technologies. The notion of bridging this gap between QoS and QoE in
NGMNs has indeed led to significant and successful works being published by network
researchers. However, much of these systems and mechanisms suggested in these works mainly
focus on presenting specific technologies. In this thesis, we introduce an independent
mechanism that is compatible enough with other technologies. The mechanism has the ability
to monitor network QoS and at the same time evaluate trends in QoE as far as user perception
of the service is concerned. We used both subjective lab environment simulations and
a simulated environment using the NETEM simulator. To generate the video streams we used
the VLC media player, a router and a switch hosted the Network Address Translator (NAT) on
the sub-net and a tool known as Distributed Internet Traffic Generator (DITG) was used to
emulate TCP bulk traffic. To emulate packet loss, Netem was our tool of choice, which was
integrated into the Linux iproute2 tool set that we also used forthe queuing disciplines
required to implement and realize the required behaviour. After this setup, we then ran several
simulations in order to study the performance of our mechanism.
According to the results, the mechanism performed as expected and each of the simulations
resulted in improved quality for the relevant users. It should be noted that, customer
differentiation presented the most expected results, which was an important aspect to Internet
Service Providers(ISPs). The simulations also showed that the perceived quality of the
users can be improved without sacrificing the quality of streams belonging to ordinary users.
The general conclusion from the implementation of these experiments indicated that its
indeed possible to monitor QoE of the users so as to retain them, this was entirely made
possible because the mechanism that was implemented can equally keep track and
evaluate network QoS in terms of video quality performance, resource utilization and
optimization. Thus addressing the major challenges that QoS and QoE pause to NGMNs as
the number of mobile video streaming users increase for different reasons. | en_US |
