Some modifs
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@ -172,13 +172,18 @@ Algorithm~\ref{d3:next-segment} details how our DASH client makes decisions.
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\SetKwFunction{Argmax}{argmax}
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\SetKwFunction{Filter}{filter}
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\SetKwFunction{EstimateNetwork}{estimate\_network\_parameters}
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\SetKwFunction{Append}{append}
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\Input{Current index $i$, time $t_i$, viewpoint $v(t_i)$, buffer of already downloaded \texttt{segments} $\mathcal{B}_i$, MPD}
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\Output{Next segment $s^{*}_i$ to request, updated buffer $\mathcal{B}_{i+1}$}
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\BlankLine{}
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(\Bw, \Rtt) \leftarrow{} \EstimateNetwork{}\;
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\Candidates\leftarrow{}\AllSegments\newline\makebox[1cm]{}.\Filter{$\Segment\rightarrow\Segment\notin\DownloadedSegments\land\Segment\in\Frustum$}\;
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\BlankLine{}
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\Candidates\leftarrow{} \AllSegments\newline\makebox[1cm]{}.\Filter{$\Segment\rightarrow\Segment\notin\DownloadedSegments$}\newline\makebox[1cm]{}.\Filter{$\Segment\rightarrow\Segment\in\Frustum$}\;
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\BlankLine{}
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\Segment\leftarrow{} \Argmax{\Candidates, \Segment\rightarrow{} $\Omega\left(\mathcal{U}(\Segment)\right)$}\;
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\DownloadedSegments.\Append{\Segment}\;
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{\caption{Algorithm to identify the next segment to query\label{d3:next-segment}}}
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\end{algorithm}
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@ -85,15 +85,15 @@ A typical 3D renderer follows Algorithm~\ref{f:renderer}.
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\SetKwFunction{Draw}{draw}
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\tcc{Initialization}
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\For{\Object\in\Scene}{%
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\LoadGeometry(\Object.\Geometry)\;
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\LoadTexture(\Object.\Texture)\;
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\For{$\Object\in\Scene$}{%
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\LoadGeometry{\Object.\Geometry}\;
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\LoadTexture{\Object.\Texture}\;
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}
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\BlankLine%
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\BlankLine%
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\tcc{Render loop}
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\While{\True}{%
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\For{\Object\in\Scene}{%
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\For{$\Object\in\Scene$}{%
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\BindTexture{\Object.\Texture}\;
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\Draw{\Object.\Geometry}\;
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}
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@ -135,8 +135,8 @@ Algorithm~\ref{f:frustum-culling} is a variation of Algorithm~\ref{f:renderer} w
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\tcc{Initialization}
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\For{$\Object\in\Scene$}{%
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\LoadGeometry(\Object.\Geometry)\;
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\LoadTexture(\Object.\Texture)\;
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\LoadGeometry{\Object.\Geometry}\;
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\LoadTexture{\Object.\Texture}\;
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}
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\BlankLine%
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\BlankLine%
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@ -58,6 +58,12 @@ This version was compiled on \today{} at \currenttime{}.
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\input{plan}
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\newpage
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\listoffigures%
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\listoftables%
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\listofalgorithms%
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\lstlistoflistings%
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\bibliography{src/bib.bib}
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\end{document}
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@ -227,30 +227,50 @@ The three first values in the supplemental property are the camera position coor
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We build on the loader introduced in Algorithm~\ref{d3:next-segment} to implement a client adaptation logic.
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We include a bookmark adaptation logic such that (i) when a bookmark is hovered for the first time, the corresponding images (see Listing~\ref{sb:bookmark-as}) are downloaded, and (ii) when a bookmark is clicked, we switch from utility $\mathcal{U}$ to optimized utility $\mathcal{U}^*$ to determine which segments to download next.
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\newcommand\mycommfont[1]{\footnotesize\ttfamily\textcolor{blue}{#1}}
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\SetCommentSty{mycommfont}
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\SetNoFillComment%
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\begin{algorithm}[th]
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\SetKwInOut{Input}{input}
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\SetKwInOut{Output}{output}
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\Input{Current index $i$, time $t_i$, viewpoint $v(t_i)$, buffer of already downloaded \texttt{segments} $\mathcal{B}_i$, MPD}
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\Output{Next segment $s^{*}_i$ to request, updated buffer $\mathcal{B}_{i+1}$}
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\SetAlgoLined%
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{- Estimate the bandwidth $\widehat{BW_i}$ and RTT $\widehat{\tau_i}$ \;}
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{- Among all \texttt{segments} that are not already downloaded $s \in \mathcal{S} \backslash \mathcal{B}_i$, % \;}
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% {-
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keep the ones inside the upcoming viewing frustums $\mathcal{FC}=\mathbb{FC}(\widehat{v}(t_i)), t\in [t_i, t_i+\chi]$ thanks to a viewpoint predictor $t_i \rightarrow \hat{v}(t_i)$, a temporal horizon $\chi$ and a frustum culling operator $\mathbb{FC}$ \;}
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\SetKw{Continue}{continue}
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\SetKwData{Bw}{bw\_estimation}
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\SetKwData{Rtt}{rtt\_estimation}
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\SetKwData{Segment}{best\_segment}
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\SetKwData{Candidates}{candidates}
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\SetKwData{AllSegments}{all\_segments}
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\SetKwData{DownloadedSegments}{downloaded\_segments}
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\SetKwData{Frustum}{frustum}
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\SetKwFunction{Argmax}{argmax}
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\SetKwFunction{Filter}{filter}
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\SetKwFunction{EstimateNetwork}{estimate\_network\_parameters}
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\SetKwFunction{Append}{append}
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\Input{Current index $i$, time $t_i$, viewpoint $v(t_i)$, buffer of already downloaded \texttt{segments}, MPD}
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\Output{Next segment to request, updated buffer}
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\BlankLine{}
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{- Optimize a criterion $\Omega$ based on $\mathcal{U}$ values and well chosen viewpoint $v(t_i)$ to select the next segment to query }
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{\begin{equation*}
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s^{*}_i= \argmax{s \in \mathcal{S} \backslash \mathcal{B}_i \cap \mathcal{FC}} \Omega_{\theta_i} \Big(\mathcal{U}(s,v(t_i))\Big) \label{sb:eq1}
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\end{equation*} \\
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given parameters $\theta_i$ that gathers both online parameters $(i,t_i,v(t_i),\widehat{BW_i}, \widehat{\tau_i}, \mathcal{B}_i)$ and offline metadata;}
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{- Update the buffer $\mathcal{B}_{i+1}$ for the next decision: $s^{*}_i$ and lowest \texttt{representations} of $s^{*}_i$ are considered downloaded\;}
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{- \Return{segment $s^{*}_i$, buffer $\mathcal{B}_{i+1}$}\;}
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\uIf{bookmark clicking}{%
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\uIf{not optimal order fetched}{%
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\Return{} optimal order segment\;
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}
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\Else{%
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\Return{} next segment\;
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}
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}
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\Else{%
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\tcc{Loading policy from previous chapter}
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(\Bw, \Rtt) \leftarrow{} \EstimateNetwork{}\;
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\BlankLine{}
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\Candidates\leftarrow{} \AllSegments\newline\makebox[1cm]{}.\Filter{$\Segment\rightarrow\Segment\notin\DownloadedSegments$}\newline\makebox[1cm]{}.\Filter{$\Segment\rightarrow\Segment\in\Frustum$}\;
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\BlankLine{}
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\Segment\leftarrow{} \Argmax{\Candidates, \Segment\rightarrow{} $\Omega\left(\mathcal{U}(\Segment)\right)$}\;
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\DownloadedSegments.\Append{\Segment}\;
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\Return\Segment;
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{\caption{Algorithm to identify the next segment to query\label{sb:next-segment}}}
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}
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\end{algorithm}
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\todo[inline]{to be modified to include bookmarks}
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