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Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, SDN, and MEC

Reza Farahani

TL;DR

This thesis goes one step beyond traditional pure client-based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.

Abstract

Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low-latency VoD and live video streaming pose new challenges to end-to-end (E2E) bandwidth demand and have stringent delay requirements. To do this, video providers typically rely on Content Delivery Networks (CDNs) to ensure that they provide scalable video streaming services. To support future streaming scenarios involving millions of users, it is necessary to increase the CDNs' efficiency. It is widely agreed that these requirements may be satisfied by adopting emerging networking techniques to present Network-Assisted Video Streaming (NAVS) methods. Motivated by this, this thesis goes one step beyond traditional pure client-based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.

Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, SDN, and MEC

TL;DR

This thesis goes one step beyond traditional pure client-based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.

Abstract

Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low-latency VoD and live video streaming pose new challenges to end-to-end (E2E) bandwidth demand and have stringent delay requirements. To do this, video providers typically rely on Content Delivery Networks (CDNs) to ensure that they provide scalable video streaming services. To support future streaming scenarios involving millions of users, it is necessary to increase the CDNs' efficiency. It is widely agreed that these requirements may be satisfied by adopting emerging networking techniques to present Network-Assisted Video Streaming (NAVS) methods. Motivated by this, this thesis goes one step beyond traditional pure client-based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.
Paper Structure (110 sections, 92 equations, 55 figures, 14 tables, 11 algorithms)

This paper contains 110 sections, 92 equations, 55 figures, 14 tables, 11 algorithms.

Table of Contents

  1. General Introduction
  2. Motivation and Problem Statement
  3. Research Questions
  4. Research Methodology
  5. Thesis Contributions
  6. Publications
  7. First Author Publications
  8. Co-authored Publications
  9. Thesis Organization
  10. Technical Background and Related Work
  11. Technical Background
  12. HTTP Adaptive Streaming (HAS)
  13. Content Delivery Networks (CDNs)
  14. Peer-to-Peer (P2P) Networks
  15. Software Defined Networking (SDN)
  16. ...and 95 more sections

Figures (55)

  • Figure 1: Global (a) IP traffic by application category and (b) Internet video by subsegment cisco2018cisco.
  • Figure 2: An example of the first research question (RQ1).
  • Figure 4: The contributions of this thesis are classified along the scientific publications conducted by the author. The small rectangles indicate which research questions (RQs) are addressed by each publication.
  • Figure 5: Differences and relations between frameworks introduced in the dissertation.
  • Figure 6: Example of an E2E HTTP adaptive live video streaming system kalan2022towards.
  • ...and 50 more figures