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+Dynamic Adaptive Streaming over HTTP (DASH) is now a widely deployed standard for video streaming, and even though video streaming and 3D streaming are different problems, many of DASH features can inspire us for 3D streaming.
+In this chapter, we present the most important contribution of this thesis: adapting DASH to 3D streaming.
+
+We start by showing how we prepare 3D data into a format that complies with DASH and that stores enough metadata to enable a client to perform efficient streaming: we partition the scene into a $k$-d tree and we further segment each cell into chunks with a fixed number of faces, which are sorted by area so that faces of a different level of detail are not grouped together.
+We also export each texture at different resolution, and we encode all the acquired metadata into a 3D version of the Media Presentation Description (MPD) that DASH uses for video.
+Namely, we store in the metadata the coordinates of the cells of the $k$-d tree, the areas of geometry chunks, and the average colors of textures.
+
+We then propose a standard client that can perform frustum culling to eliminate cells outside the viewing volume of the camera (as shown in Figure~\ref{d3:big-picture}), and we define a few utility metrics to give scores to each chunk of data, and a few streaming policies that rely on those utilities to determine which chunks need to be downloaded.
+We finally evaluate all those parameters under different bandwidth setups and compare our streaming policies.
+
+\newpage
+
 \input{dash-3d/introduction}
 \resetstyle{}