**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.

Publication# Coding Structure and Replication Optimization for Interactive Multiview Video Streaming

Abstract

Multiview video refers to videos of the same dynamic 3-D scene captured simultaneously by multiple closely spaced cameras from different viewpoints. We study interactive streaming of pre-encoded multiview videos, where, at any time, a client can request any one of many captured views for playback. Moreover, the client can periodically freeze the video in time and switch to neighboring views for a compelling look-around visual effect. We consider distributed content servers to support large-scale interactive multiview video service. These servers collaboratively replicate and access video contents. We study two challenges in this setting: what is an efficient coding structure that supports interactive view switching and, given that, what to replicate in each server in order to minimize the cost incurred by interactive temporal and view switches? We first propose a redundant coding structure that facilitates interactive view-switching, trading off storage with transmission rate. Using the coding structure, we next propose a content replication strategy that takes advantage of indirect hit to lower view-switching cost: in the event that the exact requested view is not available locally, the local server can fetch a different but correlated view from the other servers, so that the remote repository only needs to supply the pre-encoded view differential. We formulate the video content replication problem to minimize the switching cost as an integer linear programming (ILP) problem and show that it is NP-hard. We first propose an LP relaxation and rounding algorithm (termed Minimum Eviction) with bounded approximation error. We then study a more scalable solution based on dynamic programming and Lagrangian optimization (DPLO) with little sacrifice in performance. Simulation results show that our replication algorithms achieve substantially lower switching cost compared to other content replication schemes.

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts (43)

Related publications (169)

Related MOOCs (26)

Integer programming

An integer programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers. In many settings the term refers to integer linear programming (ILP), in which the objective function and the constraints (other than the integer constraints) are linear. Integer programming is NP-complete. In particular, the special case of 0-1 integer linear programming, in which unknowns are binary, and only the restrictions must be satisfied, is one of Karp's 21 NP-complete problems.

Linear programming relaxation

In mathematics, the relaxation of a (mixed) integer linear program is the problem that arises by removing the integrality constraint of each variable. For example, in a 0–1 integer program, all constraints are of the form The relaxation of the original integer program instead uses a collection of linear constraints The resulting relaxation is a linear program, hence the name.

Linear programming

Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization). More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints.

Synchrotrons and X-Ray Free Electron Lasers (part 1)

The first MOOC to provide an extensive introduction to synchrotron and XFEL facilities and associated techniques and applications.

Introduction to linear optimization, duality and the simplex algorithm.

We develop a very general version of the hyperbola method which extends the known method by Blomer and Brudern for products of projective spaces to complete smooth split toric varieties. We use it to count Campana points of bounded log-anticanonical height ...

Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...

2024, ,

We prove that the Cohn-Elkies linear programming bound for sphere packing is not sharp in dimension 6. The proof uses duality and optimization over a space of modular forms, generalizing a construction of Cohn- Triantafillou [Math. Comp. 91 (2021), pp. 491 ...