Nowadays, HTTP Adaptive Streaming (HAS) has become the de-facto standard for delivering video over the Internet. More users have started generating and delivering high-quality live streams (usually 4K resolution) through popular online streaming platforms, resulting in a rise in live streaming traffic. Typically, the video contents are generated by streamers and watched by many audiences, geographically distributed in various locations far away from the streamers. The resource limitation in the network (e.g., bandwidth) is a challenging issue for network and video providers to meet the users’ requested quality. This dissertation leverages edge computing capabilities and in-network intelligence to design, implement, and evaluate approaches to optimize Quality of Experience (QoE) and end-to-end (E2E) latency of live HAS. In addition, improving transcoding performance and optimizing the cost of running live HAS services and the network’s backhaul utilization are considered. Motivated by the mentioned issue, the dissertation proposes five contributions in two classes: optimizing resource utilization and light-weight transcoding.

1. Optimizing
QoE and Latency of
Live Video Streaming Using
Edge Computing
and
In-Network Intelligence
Alireza Erfanian
alireza.erfanian@aau.at
Supervisors:
Prof. Dr. Hermann Hellwagner and Prof. Dr. Christian Timmerer
Examiners:
Prof. Dr. Radu Prodan and Prof. Dr. Filip De Turck 1

2. Table of Contents
2
Introduction
01
Optimizing
Resource
Utilization
Contributions
02
Light-weight
Transcoding
Contributions
03
Conclusion
04

3. Introduction
3
1

4. Streaming Dominates
of total Internet
traffic in 2022,
24% increase over
2021 [1]
Video Traffic
65.9%
Heterogeneity
4
Sandvine, “Global Internet Phenomena Report January 2023,” [Online] Available: https: //www.sandvine.com/phenomena.
Devices
Platforms
Networks

5. HTTP-based Adaptive Streaming
Request Manifest/Segment
Send Manifest/Segment
Time
Quality
Time
Quality
Time
Bandwidth
HTTP server Internet Client
5

6. End-to-end Live Video Delivery Workflow
Player
Capturing
Encoding and
Packaging
(HTTP)
Origin
Server
Content Delivery
Network (CDN)
Contribution Distribution Consumption
E2E latency
6

7. Live HAS Trade-offs
E2E Latency
Lower Latency
Lower Quality and More Rebuffering
Quality of Experience (QoE)
Less Rebuffering and Higher Quality
Higher Latency
Scalability
Higher Scalability (channels, users, bitrates)
Higher Complexity
L
a
t
e
n
c
y
Q
o
E
S
c
a
l
a
b
i
l
i
t
y
7

8. Software-based Solutions
SDN Edge computing
8
NFV
In-Network
Intelligence

9. Transcoding (Transrating)
Converting video or audio content from one bitrate/format to another to
make it compatible with different devices or platforms and to meet the
varying bandwidth requirements of users
Compatibility Compatibi
lity
Cost
Adaptive
Streaming
Network
Utilization
9

10. Challenges and Research Questions
2
QoE
How to increase the clients’
QoE with a minimum
negative impact on the other
factors?
3
Latency
How can we leverage
in-network intelligence to meet
the latency requirements while
keeping the other QoE
parameters satisfactory?
1
Scalability
How can the software-based
solutions help for scalability
improvement?
10

11. Challenges and Research Questions
4
Resource
utilization
How can SDN, NFV, and edge
computing be used to
optimize live video streaming
resource utilization?
5
Cost
How can the tasks in the live
streaming workflow be done as
a chain of VNFs in the network
aiming to minimize the cost
while meeting the live streaming
requirements?
6
Transcoding
performance
How can the performance of
transcoding be improved by
utilizing in-network intelligence?
11

12. Contributions
Live HAS
Issues
Challenge 1
Scalability
Challenge 4
Resource utilization
Challenge 3
Latency
Challenge 2
QoE
Challenge 5
Cost
Challenge 6
Transcoding performance
Optimizing
resource
utilization
Contribution 1
ORAVA
Ch. 3.5
Contribution 2
OSCAR
Ch. 3.6
Contribution 3
LwTE
Ch. 4.6
Contribution 4
CD-LwTE
Ch. 4.7
Contribution 5
LwTE-Live
Ch. 4.8
Light-weight
transcoding
12

13. Optimizing
Resource
Utilization
Contributions
13
2

14. Multicast ABR
14
Cell A
Cell B
Cell C
P1 P2
P4
P5
P6
P3
QId-3
QId-0
QId-4
QId-1
QId-4
Total bandwidth consumption: 196.5 Mbps
33.3 33.3
8
8
25.3
21.2
21.2
23.1
23.1

15. ORAVA
15
A. Erfanian, F. Tashtarian, R. Farahani, C. Timmerer, and H. Hellwagner, “On optimizing resource utilization in AVC-based real-time video
streaming,” in 6th IEEE International Conference on Network Softwarization (NetSoft), Ghent, Belgium, June 2020.
VTF
VRP
Virtual Reverse Proxy
collecting clients’ requests at
the edge server, aggregating,
and sending them to the
SDN controller
hosted in PoP nodes and
preparing clients’ requests by
performing transcoding tasks
Virtual Transcoder Function
EDGE
computing

16. ORAVA
16
Cell A
Cell B
Cell C
P1 P2
P4
P5
P6
P3
QId-3
QId-0
QId-4
QId-1
QId-3
Total bandwidth consumption: 186.9 Mbps
19
8
8
21.2
21.2
23.1
23.1
SDN
Controller
R
e
q
u
e
s
t
s
I
n
f
o
.
1. origin server
2. Subsets of VTFs and PoP
nodes to host VTFs
3. Multicast tree origin=> VTFs
4. Unicast paths VTFs=>VRPs
4
3
2
1
19
44.3
OF
com
m
ands
QId-3
QId-4 QId-1
QId-0

17. OSCAR
17
Total bandwidth consumption: 167.9 Mbps
19
8
8
21.2
21.2
23.1
23.1
SDN
Controller
R
e
q
u
e
s
t
s
I
n
f
o
.
1. origin server
2. Subsets of VTFs and PoP
nodes to host VTFs
3. Multicast tree origin=> VTFs
4. Multicast tree(s) VTFs=>VRPs
4
3
2
1
P2
P4 Cell A
Cell B
Cell C
P1
P5
P6
P3
QId-3
QId-0
QId-4
QId-1
QId-3
19
25.3
OF
com
m
ands
QId-3
QId-4 QId-1
QId-0
A. Erfanian, F. Tashtarian, A. Zabrovskiy, C. Timmerer, and H. Hellwagner, “OSCAR: On Optimizing Resource Utilization in Live
Video Streaming,” IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 552–569, March 2021

18. Problem Statement
18
VRPs
(Edge of network)
Origin server
(Network core)
How can VTF placement
be optimized to minimize
transcoding cost and
bandwidth consumption?
VRPs
Origin

19. Problem Statement
19
Number of VTFs
VTFs Placement
VRPs
(Edge of network)
Origin server
(Network core)
transcoding cost
bandwidth consumption

20. Problem Statement
20
Number of VTFs
VTFs Placement
VRPs
(Edge of network)
Origin server
transcoding cost
bandwidth consumption

21. MILP Model
Inputs & Constraints:
● Network topology &
available bandwidth
● Set of origin servers
● Set of PoP nodes &
available resource
● Set of VRPs and requested
bitrates
● Given deadline MILP Optimization Model
MILP: Mixed-Integer Linear Programming
21
Outputs:
● Selected origin server
● Subsets of VTFs & PoP nodes
to host VTFs
● Multicast tree from the origin
server to VTFs
● Unicast paths from VTFs to
corresponding VRPs (ORAVA)
● Multicast trees from VTFs to
corresponding VRPs (OSCAR)
Objective function: Minimize transcoding
cost and bandwidth consumption

22. Heuristic Algorithms
The MILP models are NP-hard
Use Dijkstra algorithm
● Determine origin source node
● Creating a low-cost multicast tree from
the origin to the given VRPs
● Cost-aware VTF placement on the
obtained multicast tree
ORAVA OSCAR
Use Dijkstra algorithm
● Improve ORAVA’s heuristic Alg. time
complexity
● employ VTFs with different virtual machine
instance types
● Streams requested bitrates from VTFs to
VRPs in a multicast fashion
22

23. Performance
Evaluation
23

24. ORAVA VS OSCAR
24
Comparing ORAVA and OSCAR in terms of transcoding costs and consumed
bandwidth for different values of weight coefficient parameter (𝛂)

25. ORAVA VS OSCAR
25
Comparing ORAVA and
OSCAR in terms of
generated Open-Flow
(OF) commands for
different values of weight
coefficient parameter (𝛂)

26. Compared with SotA
26
Comparing ORAVA and OSCAR with state-of-the-art approaches in terms of (a)
consumed bandwidth, and (b) generated OF commands

27. Light-weight
Transcoding
Contributions
27
3

28. LwTE:
Light-weight
Transcoding
at the Edge IEEE
Access
A. Erfanian, H. Amirpour, F. Tashtarian, C. Timmerer, and H. Hellwagner,
“LwTE: Light-Weight Transcoding at the Edge,” IEEE Access, vol. 9, pp. 112
276–112 289, 2021. 28

29. Idea
Extract some features
as metadata during
the encoding process
Reuse metadata in
the transcoding
process at the edge
Metadata Reuse
29

30. Extracting
Metadata
30

31. Extracting Metadata
The rate distortion cost is calculated for all of these CUs to find the optimal CTU
partitioning structure with the minimum cost.
31

32. Extracting Metadata
The search to find the optimal CTU partitioning into CUs using a brute-force
approach takes the largest amount of time in the encoding process.
To avoid a brute-force search process at the edge, we extract the optimal
partitioning structure for CTUs during encoding in the origin server and store
this as metadata for each segment bitrate except the highest bitrate.
32

33. CD-LwTE:
Cost and Delay aware
Light-weight
Transcoding at the
Edge
IEEE
TNSM
A. Erfanian, H. AmirpourAzarian, F. Tashtarian, C. Timmerer, and H. Hellwagner,
“CD-LwTE: Cost-and Delay-aware Light-weight Transcoding at the
Edge,” IEEE Transactions on Network and Service Management, pp. 1–1, 2022. 33

34. Conceptual Architecture
Optimized
download/
transcode
Delivery
3
Extract
metadata
3
2
4
1
Determine
optimal
policy 3
34

35. BLP Model
Inputs & Constraints:
●Videos/Segments Size
●Metadata Size
●Resources Cost
●Available Resources
●Probability Function
●Number of Incoming Requests
BLP Optimization Model
Outputs:
● Segments’ Serving Policy
(store/transcode/fetch)
BLP: Binary Linear Programming
35
Objective function: Minimize cost (computation,
storage, bandwidth) and serving delay

36. Heuristic Algorithms
The BLP model is NP-hard
Dynamic Programming
Determines a policy for each
segments/bitrates separately
Time complexity:
O(NlogN) for initialization (only one time)
O(N) for determining policies
N: number of segments/bitrates
FGH (Fine-Grained Heur.) CGH (Coarse-Grained Heur.)
Dynamic Programming
Uses K-means clustering
Determines a policy for each cluster
Time complexity:
O(xN + N logN) for initialization (only one time)
O(xlogx + x) for determining solution
x: number of clusters and x << N 36

37. LwTE-Live:
Light-weight
Transcoding at the Edge
for Live Streaming VisNEXT’21
A. Erfanian, H. Amirpour, F. Tashtarian, C. Timmerer, and H. Hellwagner, “LwTE-Live:
Light-Weight Transcoding at the Edge for Live Streaming,” in Proceedings of the
Workshop on Design, Deployment, and Evaluation of Network-Assisted Video
Streaming, ser. VisNEXT’21. New York, NY, USA: Association for Computing
Machinery, 2021, p. 22–28. 37

38. Performance
Evaluation
38

39. Compare with x265
(a) Average bitrates of metadata relative to its corresponding representations for 4-sec. segments.
(b) Average transcoding times of “x265 with metadata” relative to “x265 without metadata”.
(a) (b)
A. Erfanian, H. AmirpourAzarian, F. Tashtarian, C. Timmerer, and H. Hellwagner, “CD-LwTE: Cost-and Delay-aware Light-weight Transcoding at the Edge,” IEEE
Transactions on Network and Service Management, 2022.
39

40. Compare with x264
(a) Compression efficiency and (b) transcoding times of x264 and LwTE (x265 with metadata
employed) for ParkRunning3, medium and veryslow presets.
40
A. Erfanian, H. AmirpourAzarian, F. Tashtarian, C. Timmerer, and H. Hellwagner, “CD-LwTE: Cost-and Delay-aware Light-weight Transcoding at the Edge,” IEEE
Transactions on Network and Service Management, pp. 1–1, 2022.
(a) (b)

41. Compare with SotA
Performance of the proposed CD-LwTE approaches compared with state-of-the-art
approaches in terms of (a) cost, and (b) average serving delay, for various ρ values (the
number of incoming requests at the edge server).
41
APAC: T. X. Tran, P. Pandey, A. Hajisami, and D. Pompili, “Collaborative multibitrate video caching and processing in Mobile-Edge Computing networks,” in 2017 13th Annual
Conference on Wireless On-demand Network Systems and Services (WONS), 2017, pp. 165–172.
CoCache: T. X. Tran and D. Pompili, “Adaptive Bitrate Video Caching and Processing in Mobile-Edge Computing Networks,” IEEE Transactions on Mobile Computing, vol. 18, no. 9,
pp. 1965–1978, 2019.
PartialCache: H. Zhao, Q. Zheng, W. Zhang, B. Du, and H. Li, “A Segment-based Storage and Transcoding Trade-off Strategy for Multi-version VoD Systems in the Cloud,” IEEE
Transactions on Multimedia, vol. 19, no. 1, pp. 149–159, 2016.
(a) (b)

42. 42
4

43. Challenge 1
Scalability
Challenge 4
Resource utilization
Challenge 3
Latency
Challenge 2
QoE
Challenge 5
Cost
Challenge 6
Transcoding performance
Contributions
Mar
OSCAR
ORAVA
Optimizing
Resource
Utilization
Light-weight
Transcoding
LwTE-Live
LwTE
CD-LwTE
43

44. Conclusions
- Leverages SDN, NFV and Edge
computing
- Introduces VRP and VTF
- Proposes an optimization
model to determine multicast
tree for live HAS, aiming to
minimize the transcoding cost
and bandwidth utilization
- Up to 78% reduction in
generated OF commands
- Up to 65% bandwidth saving
compared to state-of-the-art.
ORAVA
- Extends ORAVA by using multicast
tree(s) for streaming from VTFs to
VRPs
- Uses VTFs with different resource
profiles
- Improves the time complexity
- Reduces
OF commands up to 82%
Bandwidth consumption up to 75%
compared to state-of-the-art
approaches.
OSCAR
44

45. Conclusions
- Stores the optimal search
decisions in the encoding
process as metadata.
- Utilizes the metadata to avoid
search processes
during transcoding at the edge.
- Uses partial-transcoding.
- LwTE does transcoding 80%
faster than H.265.
- Up to 70% cost saving
compared to state-of-the-art.
LwTE
- Extends LwTE by relaxing
assumptions, new policy, and
serving delay to objective.
- Adds new features in metadata.
- BLP model to select optimal
policy to serve requests while
minimizing cost and delay.
- Reduces
transcoding time up to 97%
streaming cost up to 75%
delay up to 48%
compared to state-of-the-art
approaches.
CD-LwTE
- Investigates LwTE’s
performance in live
streaming context.
- MBLP model to select optimal
policy (fetching and transcoding)
to serve requests.
- Reduces
streaming cost up to 34%
bandwidth up to 45%
compared to state-of-the-art
approaches.
LwTE-Live
45

46. Thanks!
Do you have any questions?
alireza.erfanian@aau.at
46