Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, clients have full control over the media streaming and adaptation processes. Lack of coordination among the clients and lack of awareness of the network conditions may lead to sub-optimal user experience, and resource utilization in a pure client-based HAS adaptation scheme. Software-Defined Networking (SDN) has recently been considered to enhance the video streaming process. In this paper, we leverage the capability of SDN and Network Function Virtualization (NFV) to introduce an edge- and SDN-assisted video streaming framework called ES-HAS. We employ virtualized edge components to collect HAS clients’ requests and retrieve networking information in a time-slotted manner. These components then perform an optimization model in a time-slotted manner to efficiently serve clients’ requests by selecting an optimal cache server (with the shortest fetch time). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, or (ii) by the originally requested quality level from the origin server. This approach is validated through experiments on a large-scale testbed, and the performance of our framework is compared to pure client-based strategies and the SABR system [11]. Although SABR and ES-HAS show (almost) identical performance in the number of quality switches, ES-HAS outperforms SABR in terms of playback bitrate and the number of stalls by at least 70% and 40%, respectively.

1. ES-HAS: An Edge- and SDN-Assisted Framework for
HTTP Adaptive Video Streaming
31st
ACM NOSSDAV 2021
October 1st
, 2021
reza.farahani@aau.at | https://athena.itec.aau.at/
Reza Farahani, Farzad Tashtarian, Alireza Erfanian, 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. SABR: Network assisted content distribution for
adaptive bitrate video streaming
9
Bhat, D., Rizk, A., Zink, M. and Steinmetz, R., 2017, June. Network assisted content distribution for adaptive bitrate video streaming. In Proceedings of
the 8th ACM on Multimedia Systems Conference (pp. 62-75).

10. Motivating example
10

11. Motivating example- Exchanged messages in SABR
11
● The number of DASH clients
● The number of exchanged messages to/from
the SDN controller
● System efficiency

12. Proposed solution
12

13. Proposed solution
13
1. Use Virtual reverse proxy servers (VRP) at the edge of network
2. VRPs play the role of a gateway for the client to the network and vice versa
3. DASH clients send requests to the VRP for the desired segments’ qualities, and the VRP
collects these received requests in each time slot
4. The VRP requests the cache map and network status information from the SDN
controller for all collected requests
5. The VRP determine the optimal cache servers for the gathered clients’ requests.

14. Exchanged messages in ES-HAS
14
The number of messages to/from the SDN
controller will decrease in ES-HAS

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

16. ES-HAS Architecture
16
RAM

17. 17
SOM
ES-HAS Architecture

18. Server/Segment selection policy
18
Our server/segment policy is :
1. When the requested quality level exist in the cache servers (Cache hit)
○ find the cache server with minimum fetch time
2. When the requested quality level is not available in any cache server (Cache miss)
○ serve client with replacement quality from a cache server with minimum fetch
time
○ fetch the original requested quality from the origin server
We propose a MILP optimization model to find the optimal solution by :

19. Evaluation setup
19

20. We evaluate the performance of ES-HAS compared to SABR and pure client-based
approaches on a large-scale cloud-based testbed.
○ Sixty clients (AStream DASH Player)
○ Four cache servers (60% of the videos’ segment) and an Origin server (Apache web
server)
○ Five OpenFlow switches (Ubuntu 18.04 LTS inside Xen virtual machines)
○ An SDN controller (dockerized Floodlight)
○ Two VRP servers (Python, and Pulp with CBC solver)
○ A video Dataset including:
■ ten video sequences (BBB with 150 segments)
■ 2, 4, 6 segments
■ five representations ({0.89, .260, .790, 2.4, 4.2} Mbps)
○ 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
20

21. Experimental results
21

22. ● We analyze the behavior of ES-HAS MILP Model by :
a. MILP model execution time
b. Different numbers of segment requests and cache servers
c. Different video segment duration
Evaluation of the ES-HAS MILP Model
22

23. ● We analyze the Impact of different parameters on ES-HAS MILP model behavior by:
a. ACS : the average usage percentage of cache servers with the shortest fetch time
b. AMD: the average (for different accepted max-deviation value) of the maximum
deviation between requested quality and forwarded quality
c. AQB: the average of the video quality bitrate for all received segments in Mbps
Evaluation of the ES-HAS MILP Model
23

24. Requested quality levels vs. forwarded quality levels
24

25. Playback bitrate in different approaches:
25

26. Number of Stalls and Quality switch in different
approaches:
26

27. 27
Conclusion and Future work

28. ● This paper leverages the SDN and NFV paradigms to propose the ES-HAS framework
providing network assistance for HTTP adaptive video streaming
● We introduce components named VRPs at the edge of the network that employs a
novel server/segment policy.
● We implement the proposed framework and its modules on a cloud-based large-scale
testbed consisting of 60 clients and conducts experiments in different scenarios
● ES-HAS outperforms state-of-the-art approach in terms of playback bitrate and the
number of stalls by at least 70% and 40%, respectively.
● Edge caching, Edge collaboration, extending proposed MILP model, and utilizing
learning-based approach are possible future work directions.
Ongoing and Future Work
All rights reserved. ©2020 28

29. Thank you for your attention
reza.farahani@aau.at | https://athena.itec.aau.at/
All rights reserved. ©2020
29