% Any preprocessing that can be done on our 3D data gives us a strategical advantage since it consists in computations that will not be needed live, neither for the server nor for the client.
% Furthermore, for streaming, data needs to be split into chunks that are requested separately, so perparing those chunks in advance can also help the streaming.
The segmentation of the content into chunks is particularly important for streaming since it allows transmitting only a portion of the data to the client.
One of the questions this thesis has to answer is: \emph{what is the best way to prepare 3D content so that a streaming client can progressively download and render the 3D model?}
A chunk that contains data in the field of view of the user is more relevant than a chunk that is not inside; a chunk that is close to the camera is more relevant than a chunk far away from the camera.
This should also include other contextual parameters, such as the size of a chunk, the bandwidth and the user's behaviour.
In order to propose efficient streaming policies, we need to know \emph{how to estimate a chunk utility, and how to determine which chunks need to be downloaded depending the user's interactions?}
The quality of service is a network-centric metric, which considers values such as throughput and measures how well the content is served to the client.
The quality of experience is a user-centric metric: it relies on user perception and can only be measured by asking how users feel about a system.
To be able to know which streaming policies are best, one needs to know \emph{how to compare streaming policies and evaluate the impact of their parameters on the quality of service of the streaming system and on the quality of experience of the final user?}
The objective of our work is to setup a client-server architecture that answers the above problems: content preparation, chunk utility, streaming policies.
In this regard, we have to find out \emph{how do we build this architecture that keeps a low computational load on the server so it scales up and on the client so that it has enough resources to perform the tasks described above?}
% This implementation must respect constraints required for performant software:
%
% \begin{itemize}
% \item \textbf{for the server}, since a server must serve many clients, and a solution that requires even low computational load on the server will scale difficultly;
% \item \textbf{for the client}, since the end user will use his own device, whether it be an old computer or a mobile device, and the implementation must be efficient enough to leave resources (such as CPU or memory) for the other tasks it has to accomplish.
% \end{itemize}
%
% Furthermore, since we want to be able to evaluate our systems, user studies are required and using web technologies is a way to simplify this task.
% Therefore, part of our software needs to be runnable from a web browser.
%
% We will have to find out \emph{how do we build a client-server architecture that