1) The document presents CSDN, a framework that leverages SDN and NFV to provide network assistance for HTTP adaptive video streaming. It proposes using SDN virtual routers equipped with transcoding capabilities to optimize quality of experience (QoE) based on network conditions and user preferences. 2) An evaluation of CSDN on a testbed with 100 clients showed it improved playback bitrate by 7.5% and reduced quality switches and stalls by 19% compared to state-of-the-art approaches, enhancing user QoE and network utilization. 3) Future work directions include improving edge caching strategies, developing learning-based approaches, and extending an MILP model to optimize transcoding

1. CSDN: CDN-Aware QoE Optimization in SDN-Assisted
HTTP Adaptive Video Streaming
46th
IEEE LCN 2021
October 7th
2021
reza.farahani@aau.at | https://athena.itec.aau.at/
Reza Farahani, Farzad Tashtarian, Hadi Amirpour, Christian Timmerer, Mohammad Ghanbari, Hermann Hellwagner

2. Agenda
● Introduction
● State of the art
● Motivating example
● Proposed solution
● Evaluation setup
● Experimental results
● Conclusion and Future work

3. Introduction
3

4. ● Video traffic has become the dominant traffic over the
Internet.
● It is expected to reach more than 82% of all Internet traffic in
2021 [1].
● HTTP adaptive streaming (HAS) has been considered as the
de-facto video delivery technology over the Internet.
Introduction- Video Streaming
4
[1] Cisco. Global - 2021 Forecast Highlights. https://www.cisco.com/c/dam/m/en_us/solutions/service-provider/vni-forecast-highlights/pdf/Global_2021_Forecast_Highlights.pddf

5. ● The adaptation process can be performed with different schemes:
○ Pure client-based:
■ The decision is based on the local parameters, e.g.,
● buffer status
● estimated available bandwidth
■ Insufficient information about the network
● It can lead to a suboptimal adaptation decision
○ Network-assisted:
■ The decision is performed via a centralized network component with a global view of
the entire network topology.
■ can be more beneficial for the users’ QoE
● Fundamental paradigms of modern networks, i.e., SDN, NFV, edge computing have been
used in modern network-assisted frameworks
Introduction- Network-assisted video streaming
5

6. ● The fundamental paradigm of modern networks to
address the limitations of conventional network architecture
like:
○ Complex Network Devices
○ Management Overhead
○ Limited Scalability
● The control plane (forwarding decision) is decoupled from
the data plane (acts on the forwarding decision)
○ Centralized Network Controller
○ Standard communication Interface (OpenFlow),
○ Programmable Open APIs
Introduction-Software-Defined Networking (SDN)
6

7. ● It is considered as a complementary technology to SDN
● NFV enables Virtual Network Functions (VNFs) to
○ run over an open hardware platform
○ Reduce OpEx, CapEx
○ Accelerate innovations
Introduction-Network Function Virtualization (NFV)
7
Router
Switch Load Balancer (LB)
Firewall
Virtualization Layer
VRouter VFirewall
VSwitch VLB
VNF VNF
VNF VNF

8. State of the art
8

9. 9
Farahani, R., Tashtarian, F., Erfanian, A., Timmerer, C., Ghanbari, M. and Hellwagner, H., 2021, October. ES-HAS: An Edge- and SDN-Assisted
Framework for HTTP Adaptive Video Streaming,” in ACM NOSSDAV, 2021.(pp. 50-57).
ES-HAS: An Edge- and SDN-Assisted Framework for
HTTP Adaptive Video Streaming

10. Motivating example
10

11. Pure client-based approach
11
● Cache miss The cache server must hold the requests Fetch the requested
segments from the origin server

12. ES-HAS
12
● Demanded quality levels are available only on CS2
● CS1 with more available bandwidth could serve the requested segments with higher
quality levels
● The requested segments are unavailable in all cache servers, and the quality deviation is
unacceptable for the clients
1
2

13. Proposed solution
13

14. Proposed solution
14
● CSDN equips the ES-HAS VRP with the transcoding capability
● CSDN’s VRPs receive the network information, plus user preferences
● CSDN’s VRPs take into account:
○ fetch-based actions
○ transcoding-based actions
● Increases the computation costs of the system.
● The backhaul bandwidth consumption and users’ QoE (based on their preferences)
are significantly improved by the VRP possibly performing additional actions.

15. CSDN Architecture
15
● We leverage SDN, NFV, edge computing and propose our architecture in three layers

16. Time-slot Structure
16

17. Server/Segment selection policy
17
Our server/segment policy is :
1. When the requested quality level exist in the cache servers (Cache hit)
○ find the cache server with minimum serving time
● Original requested quality
● Transcoded quality
2. When the requested quality level is not available in any cache server (Cache miss)
○ Use replacement quality from a cache server with minimum fetch time
○ Transcode the original quality from better quality level at the edge
○ fetch the original requested quality from the origin server

18. Evaluation setup
18

19. We evaluate the performance of CSDN compared to ES-HAS, SABR and pure client-based
approaches on a large-scale cloud-based testbed.
○ 100 clients
○ Four cache servers
○ Five OpenFlow switches
○ An SDN controller
○ Four VRP servers
○ A video Dataset including:
■ ten video sequences (BBB with 150 segments)
■ 2, 4, 6 segments
■ five representations
○ Two ABR algorithms (Squad, and BOLA)
○ MongoDB for cache-map transaction
○ Different Network paths with various bandwidth
○ Bandwidth monitoring (Floodlight Restful API)
○ LRU cache replacement policy
Testbed
19

20. Experimental results
20

21. ● CSDN outperforms the state-of-the-art in terms of:
○ Playback bitrate 7.5%
○ The number of quality switches 19%
○ The number of stalls 19%
User’s QoE in different approaches:
21

22. Network utilization in different approaches:
22

23. 23
Conclusion and Future work

24. ● This paper leverages the SDN and NFV paradigms to propose the CSDN framework
providing network assistance for HTTP adaptive video streaming
● We equip ES-HAS VRPs that employs a novel server/segment selection policy
● We implement the proposed framework and its modules on a cloud-based large-scale
testbed consisting of 100 clients and conducts experiments in different scenarios
● CSDN outperforms state-of-the-art approach in terms of users’ QoE and the network
utilization
● Edge caching, extending proposed MILP model, and utilizing learning- ,
(meta)heuristic-based approach are possible future work directions.
Ongoing and Future Work
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25. Thank you for your attention
reza.farahani@aau.at | https://athena.itec.aau.at/
All rights reserved. ©2020
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