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

Network-Assisted Delivery of Adaptive Video Streaming
Services through CDN,SDN,and MEC
Supervisors:
Univ.-Prof.DI Dr.Hermann Hellwagner
Univ.-Prof.DI Dr.Christian Timmerer
Reza Farahani
Klagenfurt am Wörthersee,Austria
22.08.2023
Reviewers:
Prof.Dr.Filip De Turck
Prof.Dr.Tobias Hoßfeld

Table of Contents
-Motivation
-Technical Background
-Research Questions
-Contributions
Introduction
1
4
2
Edge-and SDN-Assisted
Frameworks for HAS
3
SFC-Enabled Architecture
for HAS
Collaborative Edge-Assisted
Frameworks for HAS
5
Hybrid P2P-CDN
Architectures for HAS
06
Conclusions
6
-Conclusions
-Publications
2

Motivation
2
3
4
5
6
1
4
● Video is dominating today’s Internet traffic
○ Video streaming includes 66% of the total video Internet traffic [1]
○ Video-on-Demand (VoD) and live streaming have become
significantly popular video streaming applications
○ Live streaming will increase 15-fold and reach 17% of Internet video
traffic [2]
[1] Sandvine, “The Global Internet Phenomena Report,” White Paper, 2023. https://www.sandvine.com/global-internet-phenomena-report-2023
[2] Cisco Visual Networking Index (VNI), Forecast and Trends, 2018– 2023. White Paper, 2018.
https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.pdf

HTTPAdaptive Video Streaming (HAS)
2
3
4
5
6
1
5
1
2
3
4
5 5
5
6
6
6 7
Encoder
Origin
Packager
CDNs
HTTP Res
HTTP Req
Reza Shokri Kalan, Reza Farahani, Emre Karsli, Christian Timmerer, and Hermann Hellwagner. Towards Low Latency Live Streaming: Challenges in a Real-world Deployment. 13th ACM MMSys, 2022.
Quality
Bandwidth
Time
Time

Network-Assisted Video Streaming (NAVS)
2
3
4
5
6
1
6
Network Assistance by HAS
Network Assistance for HAS
Network
Media Server

SFC
Modern NAVS Systems
2
3
4
5
6
1
7
SDN NFV
Hybrid Systems
NAVS Systems
Networking Paradigms
Content Delivery Networks
Emerging Protocols
Techniques
Computing Continuum Nodes
Edge
Cloud
Fog
CMAF
QUIC
LL-HAS
CMCD/SD
Transcoding
Multi Paradigms
Hybrid P2P-CDN
Caching Prefetching
CDN
P2P
Super-Resolution
Slicing

RQ1 How can SDNs/CDNs provide assistance for HAS clients in order to improve media
delivery services?
Research Questions
2
3
4
5
6
1
8
How can resources (i.e., computation, storage, bandwidth) provided by the
HAS clients be used to improve media delivery services?
RQ2

Research Questions
2
3
4
5
6
1
9
RQ3
RQ4
What is the utility of the proposed assistance and collaboration service?
How can the utility of the proposed NAVS frameworks be thoroughly evaluated,
both theoretically and practically?

Edge and SDN-Assisted Framework for HAS
2
R. Farahani, et al. "ES-HAS: An Edge-and SDN-Assisted Framework for HTTP Adaptive Video Streaming", The 31st ACM
Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV), 2021.
R. Farahani, et al. "CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming", IEEE 46th
Conference on Local Computer Networks (LCN), 2021.
13

Content Delivery Network (CDN)
14
3
4
5
6
1
2

Software Deﬁned Networking (SDN)
15
3
4
5
6
1
2
Data Plane
Control Plane
https://opennetworking.org/sdn-definition/

Network Function Virtualization (NFV)
16
3
4
5
6
1
2

SABR Framework
3
4
5
6
1
2
17
Bhat, D., Rizk, A., Zink, M. and Steinmetz, R. Network assisted content distribution for adaptive bitrate video streaming. In Proceedings of the 8th ACM MMSys, 2017.
SABR: Network assisted content distribution for adaptive bitrate video streaming

Motivation
3
4
5
6
1
2
18
Increasing the number of DASH clients ⇒ Increasing the number of exchanged
messages to/from the SDN controller

Multi-Access Edge Computing (MEC)
2
3
4
5
6
1
19
3
4
5
6
1
2

ES-HAS System
3
4
5
6
1
2
20
● ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
○ Virtual Reverse Proxy (VRP) servers at the edge of an SDN-enablebd Network

Proposed System
3
4
5
6
1
2
21

Proposed System
3
4
5
6
1
2
21
The number of messages to/from the SDN controller

ES-HAS Architecture
3
4
5
6
1
2
22

ES-HAS Server/Segment Selection Policy
3
4
5
6
1
2
23
1. RAM
2. SOM
Fetching Time
Quality Deviation
Quality Bitrate

ES-HAS Testbed
3
4
5
6
1
2
24
● A cloud-based testbed, including 60 clients (Xen virtual machines) on CloudLab [1]
[1] https://www.cloudlab.us/

CSDN System
3
4
5
6
1
2
26
● CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
○ It equips the ES-HAS VRP with the transcoding capability

CSDN Server/Segment Selection Policy
3
4
5
6
1
2
28
Serving Time
Quality Deviation
1. When the requested quality level exist in the cache servers (Cache Hit)
○ ﬁnd the cache server with minimum serving time
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

CSDN Testbed
3
4
5
6
1
2
29
● Extended ES-HAS testbed with 100 clients (Xen virtual machines) on CloudLab [1]
[1] https://www.cloudlab.us/

SFC-Enabled Architecture for HAS
3
R. Farahani, et al. "SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications", IEEE International Conference
on Communications (ICC), 2023.
32

Motivation
4
5
6
1
2
3
33
● OTT video
● Live video streaming
● Immersive multimedia
● Video Gaming
● Video analytics for security,
quality assurance, etc.
Increase in amount of video
generated and transported

Motivation
4
5
6
1
2
3
33
● OTT video
● Live video streaming
● Immersive multimedia
● Video Gaming
● Video analytics for security,
quality assurance, etc.
Increase in amount of video
generated and transported
Versatile QoE, QoS requirements
Resolution (4K, 8K)
Latency (LL,ULL)
Bitrate

Motivation
4
5
6
1
2
3
34
SDN
S
F
C
HAS
M
E
C
● How to leverage modern networking/computing paradigms to serve different MSs
requests with acceptable QoE and improved network utilization?
● How to design a network-assisted HAS scheme without client-side modiﬁcation ?

Service Function Chaining (SFC)
4
5
6
1
2
3
35
VNF i VNF i+1 VNF n
VNF i VNF i+1 VNF n
SFC
Chains
Chain
1
Chain
m
…
…
…

Service Function Chaining (SFC)
4
5
6
1
2
3
35
VNF i VNF i+1 VNF n
VNF i VNF i+1 VNF n
SFC
Chains
Chain
1
Chain
m
…
…
…
Orchestration
Placement
Scheduling
SFC
Definition
VNF
Definition

SARENAArchitecture
4
5
6
1
2
3
36
Virtual Proxy Function
Virtual Cache Function
Virtual Transcoding Function
CDN Cache
Origin Cache
1
2
3
4
5
Multimedia
VNFs

SARENAArchitecture
4
5
6
1
2
3
37
3
1
2
5
Multimedia
SFCs
1
2
4
1
1
4
1 3

SARENA Optimization Model
4
5
6
1
2
3
38
● The Requests Scheduler run an MILP optimization model to respond:
○ Where is the optimal place for fetching the content quality level requested by each
client, while efﬁciently employing layers’ available resources and satisfying service
requirements (e.g., service deadlines)?
○ How can we use the functions/services provided in the EL and IL layers to form MS
function chains (SFCs)?
○ What is the optimal SFC for responding to the requested quality level with speciﬁc
service requirements?
Total MSs Serving Latency
Transmitting Time
Transcoding Time

SARENA Heuristic Solution
4
5
6
1
2
3
39
Virtual Scheduler Function

SARENATestbed
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5
6
1
2
3
40
A cloud-based testbed, including 280 elements and real backbone topology (InternetMCI)
○ Xen virtual machines
○ 250 Dash player
○ Four Apache cache servers and an origin server
○ 19 backbone switches and 45 layer-2 links
○ Five edge server
○ Floodlight SDN controller
○ BOLA ABR algorithms
○ FFmpeg transcoders
○ LRU cache replacement policy
○ Zipf distribution is used for video and channel access popularity

SARENATestbed
4
5
6
1
2
3
41
0.089
320
480
720
1080
1080
0.262
0.791
2.4
4.2
Resolution (p) Bitrate (Mbps) Bitrate (Mbps)
Resolution (p)
20
VoDs,
300
sec.
duration,
4
sec.
segments
320
480
720
720
1080
1080
1080
0.128
0.32
0.78
1.4
2.4
3.3
3.9
5
live
ch,
300
sec.
duration,
2
sec.
segments

Evaluation Methods and Metrics
4
5
6
1
2
3
41
✔ Baseline systems:
◆ CDN-assisted (CDA)
◆ Non VNF-assisted (NVA)
◆ Non VTF-enabled (NTE)
◆ Non Reconﬁguration-enabled (NRE)
✔ The performance of the aforementioned approaches is evaluated through
◆ ASB: Average Segment Bitrate
◆ AQS: Average Number of Quality Switches
◆ ANS: Average Number of Stalls
◆ ASD: Average Stall Duration
◆ APQ: Average Perceived QoE calculated by ITU-T P.1203 mode 0 [1]
◆ ASL: Overall time for serving
◆ NCV: Network Cost Value
◆ ETR: Edge/P2P Transcoding Ratio
◆ BTL: Backhaul Trafﬁc Load
[1] https://www.itu.int/net4/ipr/details_ps.aspx?sector=ITU-T&id=P1203-01

SARENA Results
4
5
6
1
2
3
42
ASB: Average Segment Bitrate
AQS: Average Number of Quality
Switches
ANS: Average Number of Stalls
ASD: Average Stall Duration
APQ: Average Perceived QoE
calculated by ITU-T P.1203 mode 0 [1]
ASL: Average Serving Latency

SARENA Results
4
5
6
1
2
3
43
NCV: Network Cost Value
ETR: Edge Transcoding Ratio
BTL: Backhaul Trafﬁc Load

Collaborative Edge-Assisted Frameworks for HAS
4
R. Farahani, et al., "LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video
Streaming". IEEE International Conference on Communications (ICC), 2022.
R. Farahani, et al., "ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming", IEEE
Transactions on Network and Service Management (TNSM), 2022.

5
6
1
2
3
4
45
Motivation
✔ Establish a collaboration between edge servers to use their potential idle
resources for serving HAS clients.
M
E
C
SDN
N
F
V
H
A
S

5
6
1
2
3
4
46
Proposed Collaborative Systems

5
6
1
2
3
4
47
LEADER/ARARATARCHITECTURE
✔ MEC Servers:
◆ Local Edge Server (LES)
◆ Neighboring Edge Server (NES)
✔ Virtualized Edge Functions:
◆ Partial Cache (PC)
◆ Video Transcoder (Tran)
Edge Layer

5
6
1
2
3
4
47

5
6
1
2
3
4
47
cache_map
Requests, edge_map, comp_map

LEADER and ARARATAction Tree
4
5
6
1
2
3
48
5
6
1
2
3
4
LEADER Action Tree ARARAT Action Tree

LEADER/ARARAT Optimization Models
4
5
6
1
2
3
49
● The SDN controller run an MILP optimization model to respond:
○ Where is the optimal place (i.e., LES, NESs, CSs, or the origin server) in terms of the
following items for fetching each client’s requested content quality level from?
■ minimum serving time (LEADER)
■ minimum serving time and minimum network cost (ARARAT)
○ What is the optimal approach for responding to the requested quality level (i.e., fetch
or transcode)?
Serving Latency
5
6
1
2
3
4
Network Cost
LEADER ARARAT

ARARAT Coarse-Grained (CG) Heuristic
4
5
6
1
2
3
49
5
6
1
2
3
4
COM → LOM

ARARAT Fine-Grained I (FG I) Heuristic
4
5
6
1
2
3
50
5
6
1
2
3
4
LOM → EFG I

ARARAT Fine-Grained I (FG I) Heuristic
4
5
6
1
2
3
50
5
6
1
2
3
4
LOM → EFG I
Network bandwidth allocation?

ARARAT Fine-Grained II (FG II) Heuristic
4
5
6
1
2
3
51
5
6
1
2
3
4
EFG I → EFG II

ARARAT Fine-Grained II (FG II) Heuristic
4
5
6
1
2
3
52
5
6
1
2
3
4
The bandwidth allocation is modeled as a “Fairness LP Optimization Model”

ARARATTestbed
1
53
● A cloud-based testbed, including 301 elements (Xen virtual machines) and real backbone topology
(Geant and Abilene)
4
5
6
1
2
3
5
6
1
2
3
4

1
54
4
5
6
1
2
3
5
6
1
2
3
4
Evaluation Methods
✔ SABR:
◆ Non edge-enabled system
✔ ES-HAS
◆ Non edge-collaborative and transcoding-enabled system
◆ Each edge runs an MILP model on the collected client requests to serve them via one of actions
1, 7, or 9
✔ CSDN
◆ Non-collaborative approach
◆ Each edge server runs an MILP model for the collected client requests and serves them
separately via one of the actions 1, 2, 7, 8 or 9
✔ NECOL
◆ The Non Edge Collaborative (NECOL) system does not support an edge collaboration.
◆ Each NECOL edge server executes a simpliﬁed version of the proposed FG I heuristic for
◆ each client request to serve it through one of the actions 1, 2, 7, 8 or 9
✔ DECOL
◆ Default Edge Collaborative (DECOL)
◆ Run the proposed FG I heuristic

1
55
4
5
6
1
2
3
5
6
1
2
3
4
LEADER Results
AQS: Average Number of Quality Switches
APQ: Average Perceived QoE calculated by
ITU-T P.1203 mode 0 [1]
CHR: Cache Hit Ratio

1
56
4
5
6
1
2
3
5
6
1
2
3
4
ARARAT Results
AQS: Average Number of Quality
Switches
APQ: Average Perceived QoE
calculated by ITU-T P.1203 mode 0 [1]

1
57
4
5
6
1
2
3
5
6
1
2
3
4
ARARAT Results
ANU: Average Network Utilization
ASL: Average Serving Time
ANC: Average Number of Communicated
messages from/to the SDN controller

Hybrid P2P-CDN Architectures for HAS
5
R. Farahani, et al., "Hybrid P2P-CDN Architecture for Live Video Streaming: An Online Learning Approach”. IEEE Global
Communications Conference (GLOBECOM), 2022.
R. Farahani, et al., "RICHTER: Hybrid P2P- CDN Architecture for Low Latency Live Video Streaming”. ACM Mile-High Video
Conference (MHV), 2022.
R. Farahani, et al., "ALIVE: A Latency- and Cost-Aware Hybrid P2P-CDN Framework for Live Video Streaming", submitted to
IEEE Transactions on Network and Service Management (TNSM), 2023.

Peer-to-Peer Network (P2P)
59
6
1
2
3
4
5
Tracker

Motivation
6
1
2
3
4
5
60
✔ Employ all potential resources (storage, bandwidth, computation) of the P2P network
✔ Utilize P2P and CDN resources efﬁciently
✔ Satisfy HAS client requests with acceptable QoE and improved latency
CDN P2P

61
Proposed Collaborative Systems
6
1
2
3
4
5

ALIVE Architecture
6
1
2
3
4
5
62
✔ RICHTER does not include the PSR function.

RICHTER and ALIVE Action Tree
63
RICHTER Action Tree ALIVE Action Tree
6
1
2
3
4
5

RICHTER/ALIVE Optimization Models
4
5
6
1
2
3
64
● The VTS server must respond:
○ Where is the optimal place (i.e., adjacent peers, VTS, CDN servers, or origin server) in
terms of the following items for fetching each client’s requested content quality level
from?
■ minimum serving time (RICHTER)
■ minimum serving time and minimum network cost (ALIVE)
○ What is the optimal approach for responding to the requested quality level (i.e., fetch
,transcode, or upscale)?
Serving Latency
5
6
1
2
3
4 Network Cost
RICHTER ALIVE
6
1
2
3
4
5

RICHTER Online Learning (OL) Approach
4
5
6
1
2
3
65
● Leverage new modules, classification technique to introduce an OL heuristic approach
● Self Organizing Map (SOM) is adopted as the request management solution in the OL agent:
○ popular technique for unsupervised classification problems
○ can be applied to solve NP-hard problems
○ does not require a prepared dataset for supervised model training
● Alive runs a simplified version that is based on the Greedy approach
5
6
1
2
3
4
6
1
2
3
4
5
Node
Action
Req#
Violation

ALIVE Greedy-Based Heuristic Approach
4
5
6
1
2
3
65
5
6
1
2
3
4
6
1
2
3
4
5

ALIVE Testbed
4
5
6
1
2
3
66
5
6
1
2
3
4
6
1
2
3
4
5
● A cloud-based testbed, including 375 elements (Xen virtual machines) and real backbone
topology (InternetMCI)
● Apple M1, Xiaomi Mi11, and iPhone 11

67
Evaluation Methods
✔ NOH:
◆ Non Hybrid system like traditional CDN based methods
✔ SEH:
◆ Simple Edge-enabled Hybrid
◆ It employs a simple VTS server without caching and transcoding capabilities
✔ NTH
◆ Non Transcoding-enabled Hybrid
◆ It has a VTS server with only caching capability
✔ ECT
◆ Edge Caching/Transcoding Hybrid
◆ It does not include transcoding and super-resolution at the peer side
✔ NSH
◆ Non SR-enabled Hybrid (RICHTER using Greedy apprach)
◆ There is no super-resolution feature on the peer side
4
5
6
1
2
3
5
6
1
2
3
4
6
1
2
3
4
5

68
P2PTR/SR Results
4
5
6
1
2
3
5
6
1
2
3
4
6
1
2
3
4
5

69
RICHTER Results
4
5
6
1
2
3
5
6
1
2
3
4
6
1
2
3
4
5
ITU-T P.1203 mode 0 [1]

70
ALIVE Results
4
5
6
1
2
3
5
6
1
2
3
4
6
1
2
3
4
5
ITU-T P.1203 mode 0 [1]

71
ALIVE Results
4
5
6
1
2
3
5
6
1
2
3
4
6
1
2
3
4
5
PTSR: Peer SR and TR Ratio
EEC: Edge Energy Consumption for running
ETR

Conclusions
How can SDNs/CDNs provide assistance for HAS clients in
order to improve media delivery services?
RQ1
1
2
3
4
5
6 73
● ES-HAS and CSDN frameworks
○ Introduce Virtual Reverse Proxy function to act as a gateway between HAS
clients and the network
○ Design new server/segment selection policies
● SARENA framework
○ Design VNFs that can be chained under an SDN controller’s coordination to
establish different types of video streaming services
● LEADER and ARARAT frameworks
○ Establish a collaboration between edge servers under an SDN controller’s
coordination
○ Propose Action Tree including all possible actions to serve requests
○ Propose fair bandwidth allocation startegies

Conclusions
How can resources (i.e., computation, storage, bandwidth)
provided by the HAS clients be used to improve media delivery
services?
RQ2
1
2
3
4
5
6 74
● RICHTER and ALIVE frameworks
○ Hybrid P2P-CDN NAVS systems for HAS clients in live streaming
applications
○ Employing idle computational resources and available bandwidth of HAS
clients (i.e., peers) to offer distributed video processing services, such as video
transcoding and video super- resolution.
○ Propose Action Tree including all possible actions to serve requests
○ Propose heuristic algorithms to play decision-maker roles in large-scale
practical scenarios.

Conclusions
What is the utility of the proposed assistance and
collaboration service?
RQ3
1
2
3
4
5
6 75
● User QoE metrics (application QoS)
○ quality bitrate, number of quality switches, number of stalls, stalling duration,
serving times, VMAF values, standardized perceived quality.
● Network utilization metrics
○ cache hit ratio, transcoding ratio at the edge, transcoding ratio at the P2P
network, super-resolution ratio at the P2P network, computational cost,
backhaul bandwidth cost, number of communicated messages to/from the
SDN controller.
● Algorithm performance metrics
○ execution times and objective function values

Conclusions
RQ4
1
2
3
4
5
6 76
How can the utility of the proposed NAVS frameworks be
thoroughly evaluated, both theoretically and practically?
● Desine cloud-based testbeds to run realistic network topologies for all
proposed frameworks
○ Use tens/hundreds of elements, each of which runs Linux-based operating
systems inside Xen virtual machines.
○ Use two different ABR algorithm
○ Employ bitrate ladders of real video datasets
○ Use OpenFlow backbone switches and Floodlight SDN controller
○ Use realistic network traces, tools, and assumptions
○ Compare results with state-of-the-art and baseline approaches

First Author Publications
77
1. R. Farahani, E. Cetinkaya, C. Timmerer, M. Shojafar, M. Ghanbari, H. Hellwagner. ALIVE: A Latency- and Cost-Aware Hybrid P2P-CDN
Framework for Live Video Streaming. Submitted to IEEE Transactions on Network and Service Management (TNSM), 2023.
2. R. Farahani, C. Timmerer, H. Hellwagner. Towards Low-Latency and Energy-Efficient Hybrid P2P-CDN Live Video Streaming.
Submitted to IEEE COMSOC MMTC Communication-Frontiers, 2023.
3. R. Farahani, M. Shojafar, C. Timmerer, F. Tashtarian, M. Ghanbari, H. Hellwagner. ARARAT: A Collaborative Edge-Assisted Framework
for HTTP Adaptive Video Streaming. IEEE Transactions on Network and Service Management (TNSM), 2022.
4. R. Farahani, A. Bentaleb, C. Timmerer, M. Shojafar, R. Prodan, H. Hellwagner. SARENA: SFC-Enabled Architecture for Adaptive Video
Streaming Applications. IEEE International Conference on Communications (ICC), 2022.
5. R. Farahani, A. Bentaleb, E. Cetinkaya, C. Timmerer, R. Zimmermann, H. Hellwagner. Hybrid P2P-CDN Architecture for Live Video
Streaming: An Online Learning Approach. IEEE Global Communications Conference (GLOBECOM), 2022.
6. R. Farahani, F. Tashtarian, C. Timmerer, M. Ghanbari, H. Hellwagner. LEADER: A Collaborative Edge- and SDN-Assisted Framework for
HTTP Adaptive Video. IEEE International Conference on Communications (ICC), 2022.
7. R. Farahani, F. Tashtarian, H. Amirpour, C. Timmerer, M. Ghanbari, H. Hellwagner. CSDN: CDN-Aware QoE Optimization in SDN-Assisted
HTTP Adaptive Video Streaming. 46th IEEE Conference on Local Computer Networks (LCN), 2021.
8. R. Farahani, F. Tashtarian, A. Erfanian, C. Timmerer, M. Ghanbari, H. Hellwagner. ES-HAS: an edge- and SDN-assisted framework for
HTTP adaptive video streaming. 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV),
2021.
9. R. Farahani. CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming. 12th ACM Multimedia Systems
Conference (MMSys), 2021.
10. R. Farahani, A. Bentaleb, M. Shojafar, H. Hellwagner. CP-Steering: CDN- and Protocol-Aware Content Steering Solution for HTTP
Adaptive Video Streaming. ACM Mile High Video (MHV), 2023.
11. R. Farahani, H. Amirpour, F. Tashtarian, A.Bentaleb, C. Timmerer, H. Hellwagner, and R. Zimmermann. RICHTER: Hybrid P2P-CDN
Architecture for Low Latency Live Video Streaming. ACM Mile-High Video (MHV), 2022.

Co-authored Publications
78
1. V. V Menon, R. Farahani, P. T Rajendran, H. Hellwagner, M. Ghanbari, C. Timmerer. Reduced Reference Transcoding Quality
Prediction for Video Streaming Applications. ACM Mile High Video (MHV), 2023.
2. V. V Menon, P. T Rajendran, R. Farahani, K. Schöffmann, C. Timmerer. Video Quality Assessment with Texture Information Fusion
for Streaming Applications. Submitted to the IEEE International Conference on Visual Communications and Image Processing (VCIP),
2023.
3. V. V Menon, R. Farahani, P. T Rajendran, S. Afzal, K. Schöffmann, C. Timmerer. Energy-Efficient Multi-Codec Bitrate-Ladder
Estimation for Adaptive Video Streaming. Submitted to the IEEE International Conference on Visual Communications and Image
Processing (VCIP), 2023.
4. S. Chellappa, R. Farahani, R. Bartos, H. Hellwagner. Context-Aware HTTP Adaptive Video Streaming Utilizing QUIC’s Stream Priority.
ACM Mile High Video (MHV), 2023.
5. A. Bentaleb, R. Farahani, F. Tashtarian, H. Hellwagner, R.Zimmermann. Which CDN to Download From? A Client and Server
Strategies. ACM Mile High Video (MHV), 2023.
6. R. Shokri Kalan, R. Farahani, E. Karsli, C. Timmerer, H. Hellwagner. Towards Low Latency Live Streaming: Challenges in
Real-World Deployment. The 13th ACM Multimedia Systems Conference (MMSys), 2022.
7. F. Tashtarian, A. Bentaleb, R. Farahani, M. Nguyen, C. Timmerer, H.Hellwagner, R. Zimmermann. A Distributed Delivery Architecture
for User Generated Content Live Streaming over HTTP. IEEE 46th Conference on Local Computer Networks (LCN), 2021.
8. A. Erfanian, F. Tashtarian, R. Farahani, C. Timmerer, H. Hellwagner. On Optimizing Resource Utilization in AVC-based Real-time
Video Streaming. The 6th IEEE Conference on Network Softwarization (NetSoft), 2020.

Future Directions
Trafﬁc Encryption
Immersive Streaming
Emerging Protocols
Grean Streaming

Statistics Calculation
IM= (New Value - Old Value) / Old Value

ES-HAS Results
3
4
5
6
1
2
● We analyze the Impact of different parameters on ES-HAS MILP model behavior by:
○ ACS : the average usage percentage of cache servers with the shortest fetch time
○ AMD: the average (for different accepted max-deviation value) of the maximum
deviation between requested quality and forwarded quality
○ AQB: the average of the video quality bitrate for all received segments in Mbps

Problem Formulation
Central MILP Optimization Model
Constraints & Objective Function
✓ Resource Map
✓ Requests
✓ Videos Information
✓ Computational Cost
Optimal action for each request

Fine-Grained II (FG II)-- Bandwidth Allocation Strategy
BwAllocation request
Minimum fairness value among
all fairness coefficient.
✔ The bandwidth allocation is modeled as a “Fairness LP Optimization Model”
The fairness coefficient to the shared link(a, b)
in the route between i and j.
The allocated bandwidth to the shared link(a, b)
in the route between i and j.
98

Evaluation Results-- Scenario I
✔ This scenario compares the proposed centralized optimization MILP model and the CG and FG
approaches in terms of:
◆ ETV: Execution Time Values for the different ARARAT schemes.
◆ NOV: Normalized Objective Value for the ARARAT schemes.

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