Algorithms for Scalable Content Distribution

  • Ernst W. Biersack
  • Anwar Al Hamra
  • Guillaume Urvoy-Keller
  • David Choi
  • Dimitrios N. Serpanos
  • Apostolos Traganitis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2856)


In this chapter, we address how to achieve scalable content distributions. We present two contributions, each of which uses a different approach to distribute the content.

In the first part of this chapter, we consider a terrestrial overlay network and build on top of it a VoD service for fixed clients. The goal is to minimize the operational cost of the service. Our contibutions are as follows. First, we introduce a new video distribution architecture that combines open-loop and closed-loop schemes. This combination makes the overall system highly scalable, very cost-effective, and ensures a zero start-up delay. Our second contribution is a mathematical model for the cost of delivering a video as a function of the popularity of that video. Our analytical model, along with some extensions, allows us to explore several scenarios: (i) long videos of 90 min (movies), (ii) short videos of a few min (clips), (iii) the dimensioning of a video on demand service from scratch, and (iv) the case of the optimization of an already installed video on demand service (i.e. the limited resources scenario).

In the second part of this chapter, we consider a satellite distribution of contents to mobile users, or in general to users thar are occasionally connected. We consider a push-based model, where the server periodically downloads objects. We assume that clients register to the service off-line. Our goal is to minimize the mean aggregate reception delay over all objects where each object is weighted by its popularity. Our contibutions in this part are as follows. First, we provide a simple proof for the need of periodicity (equal distance in transmission) of popular objects in a cycle. Second, in contrast to existing results, we consider the scheduling problem for caching clients. To increase the performance of the system, we also evaluate a pre-emptive scheduling algorithm that allows interruption (pre-emption) of an object’s transmission in order to transmit on schedule another more popular one.


Multicast Tree Server Cost Broadcast System Video Popularity Tuning Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ernst W. Biersack
    • 1
  • Anwar Al Hamra
    • 1
  • Guillaume Urvoy-Keller
    • 1
  • David Choi
    • 1
  • Dimitrios N. Serpanos
    • 2
  • Apostolos Traganitis
    • 3
  1. 1.Institut EurecomDepartement of Corporate CommunicationsSophia AntipolisFrance
  2. 2.Department of Electrical and Computer EngineeringUniversity of Patras 
  3. 3.Department of Computer ScienceUniversity of Crete 

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