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Collaborative Edge-Assisted Systems for HTTP Adaptive
Video Streaming
Christian Doppler laboratory ATHENA, Institute for Information Technology, Alpen-Adria-Universität Klagenfurt, Austria
January 6th
, 2023
reza.farahani@aau.at | https://athena.itec.aau.at/ | https://www.rezafarahani.me
Reza Farahani
Agenda
● Introduction
● Network-Assisted Video Streaming Solutions
● Modern Networking Paradigms
● ARARAT: A Collaborative Edge-Assisted Framework
for HTTP Adaptive Video Streaming
● Conclusion
Introduction
● Video streaming is the predominant today’s Internet traffic.
Motivation
1
Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.)
[1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport
● Video streaming is the predominant today’s Internet traffic.
Motivation
1
Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.)
Source: Sandvine GIPR Jan. 2022.
[1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport
[2] Sandvine, “The Global Internet Phenomena Report”, White Paper, January 2022. [Online]. Available: SandvineReport
● Video streaming is the predominant today’s Internet traffic.
Motivation
1
Streaming video service providers’ share of total video traffic in networks (Source: Ericsson Mobility Report Nov. 2022.)
Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.)
Source: Sandvine GIPR Jan. 2022.
[1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport
[2] Sandvine, “The Global Internet Phenomena Report”, White Paper, January 2022. [Online]. Available: SandvineReport
Video Downloading vs. Streaming
2
Video Downloading vs. Streaming
2
Video Streaming Applications
3
Video Streaming Applications
4
● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video
streams [1]
HTTP Adaptive Video Streaming (HAS)
5
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video
streams [1]
HTTP Adaptive Video Streaming (HAS)
5
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video
streams [1]
HTTP Adaptive Video Streaming (HAS)
5
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
HTTP Adaptive Video Streaming (HAS)
6
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
Adaptation (ABR) logic is within
the client, not normatively
specified by a standard, subject
to research and development
Network-Assisted Video
Streaming Solutions
Network-Assisted Video Streaming Solution (NAVS)
7
Network Assistance by HAS
Network Assistance for HAS
Network
Media Server
Network-Assisted Video Streaming Solutions (NAVS)
8
SDN NFV MEC
Hybrid Systems
NAVS
Systems
5G/6G Paradigms
Content Delivery Networks
Emerging Protocols
ML/RL supports
SFC
Computing Continuum Facilities
Edge
Cloud
Fog
CMAF
QUIC LL-HAS
CMCD/SD
Transcoding
Multi Paradigms
Hybrid P2P-CDN
Caching
Prefetching
9
Our Network-Assisted Solutions
● 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.
● R. Farahani, A. Bentaleb, E. Cetinkaya, C. Timmerer, R. Zimmermann, and H. Hellwagner. Hybrid P2P-CDN Architecture
for Live Video Streaming: An Online Learning Approach. IEEE Global Communications Conference (GLOBECOM),
2022.
● 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.
● 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.
● R. Farahani, F. Tashtarian, H. Amirpour, C. Timmerer, M. Ghanbari, and H. Hellwagner. CSDN: CDN-Aware QoE
Optimization in SDN-Assisted HTTP Adaptive Video Streaming. IEEE 46th Conference on Local Computer Networks
(LCN), 2021.
● R. Farahani, F. Tashtarian, A. Erfanian, C. Timmerer, M. Ghanbari, and H. Hellwagner. 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. CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming. The 12th ACM
Multimedia Systems Conference (MMSys), 2021.
● 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. IEEE Transactions on Mobile Computing (TMC).
10
● R. Farahani, A. Bentaleb, C. Timmerer , M. Shojafar, R. Prodan, and H. Hellwagner. SARENA: SFC-Enabled Architecture
for Adaptive Video Streaming Applications. IEEE International Conference on Communications (ICC), 2023.
● R. Farahani, A. Bentaleb, M. Shojafar, C. Timmerer, H. Hellwagner. CP-Steering: CDN- and Protocol-Aware Content
Steering Solution for HTTP Adaptive Video Streaming. ACM Mile High Video (MHV), 2023.
● R. Farahani, V. V Menon, P. T Rajendran, H. Hellwagner, M. Ghanbari, C. Timmerer. Transcoding Quality Prediction for
Adaptive Video Streaming. ACM Mile High Video (MHV), 2023.
● V. V Menon, R. Farahani, P. T Rajendran, H. Hellwagner, M. Ghanbari, C. Timmerer. Reduced Reference Transcoding
Quality Prediction for Video Streaming Applications. IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 2023.
● S. Chellappa, R. Farahani, R. Bartos, C. Timmerer, H. Hellwagner. Context-Aware HTTP Adaptive Video Streaming
Utilizing QUIC’s Stream Priority. ACM Mile High Video (MHV), 2023.
● A. Bentaleb, R. Farahani, F. Tashtarian, C. Timmerer, H. Hellwagner, R.Zimmermann. Which CDN to Download From? A
Client and Server Strategies. ACM Mile High Video (MHV), 2023.
● R. Shokri Kalan, R. Farahani, E. Karsli, C. Timmerer, and H. Hellwagner. Towards Low Latency Live Streaming:
Challenges in Real-World Deployment. The 13th ACM Multimedia Systems Conference (MMSys), 2022.
● F. Tashtarian, A. Bentaleb, R. Farahani, M. Nguyen, C. Timmerer, H.Hellwagner, and R. Zimmermann. A Distributed
Delivery Architecture for User Generated Content Live Streaming over HTTP. IEEE 46th Conference on Local Computer
Networks (LCN), 2021.
● A. Erfanian, F. Tashtarian, R. Farahani, C. Timmerer, and H. Hellwagner. On Optimizing Resource Utilization in
AVC-based Real-time Video Streaming. The 6th IEEE Conference on Network Softwarization (NetSoft), 2020.
Our Network-Assisted Solutions
11
● This talk covers the following frameworks as Collaborative Edge-Assisted Systems for HTTP Adaptive Video
Streaming:
1. LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video (IEEE ICC 2022)
2. ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming (IEEE TNSM 2022)
[1] DOI: 10.1109/ICC45855.2022.9838949
[2] DOI: 10.1109/TNSM.2022.3210595
Our Network-Assisted Solutions
5G/6G Paradigms SDN NFV MEC
11
● This talk covers the following frameworks as Collaborative Edge-Assisted Systems for HTTP Adaptive Video
Streaming:
➢ LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video (IEEE ICC 2022)
➢ ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming (IEEE TNSM 2022)
➢ Hybrid P2P-CDN Architecture for Live Video Streaming: An Online Learning Approach (IEEE GLOBECOM 2022)
Our Network-Assisted Solutions
5G/6G Paradigms
Hybrid Systems
SDN NFV MEC
P2P NFV
CDN MEC
Modern Networking Paradigms
✔ Traditional network architecture:
◆ Complex Network Devices
◆ Management Overhead
◆ Limited Scalability
Software-Defined Networking (SDN)
12
Data Plane
Control Plane
✔ Conventional network architecture:
◆ 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
Software-Defined Networking (SDN)
12
Source: https://opennetworking.org/sdn-definition/
Data Plane
Control Plane
✔ Complementary technology to SDN
✔ Network Functions Virtual Network Functions (VNFs):
◆ run over an open hardware platform
◆ Reduce OpEx, CapEx
◆ accelerate innovations
Introduction-Network Function Virtualization (NFV)
13
Router
Switch Load Balancer (LB)
Firewall
Virtualization Layer
VRouter VFirewall
VSwitch VLB
VNF VNF
VNF VNF
✔ CDN edge servers:
✔ Multi-access Edge Computing (MEC):
◆ It provides storage and computational resources close to end-users at the network's edge, reducing
● network latency
● bandwidth consumption
◆ Edge servers include limited resources (computational, storage, and bandwidth)
Edge Computing
14
MEC server
gNodeB
Origin server
Peer to Peer (P2P)
15
✔ Alleviate network congestion
✔ Increase streaming stability
✔ Reduce delivery costs
Tracker
Peers
ARARAT: A Collaborative Edge-Assisted Framework
for HTTP Adaptive Video Streaming
15
Research Questions (RQs)
✔ How to use edge resources efficiently to optimize users’ QoE and network utilization?
✔ How to design an edge- and SDN-assisted HAS framework for video optimization purposes?
✔ How to establish a collaboration between edge servers to use their potential idle resources for
serving HAS clients.
✔ How to design a network-assisted HAS scheme without client-side modification ?
✔ How we can implement and evaluate proposed approach in a large-scale testbed?
SDNN
F
V
HAS
M
E
C
16
System Architecture-- Edge Layer
Edge Layer
✔ Edge Servers:
◆ Local Edge Server (LES)
◆ Neighboring Edge Server (NES)
✔ Edge Functions:
◆ Partial Cache (PC)
◆ Video Transcoder (Tran.)
MEC
NFV
17
System Architecture-- CDN/Origin Layer
✔ Multi-CDN Servers
✔ Origin Server
18
System Architecture-- Control Layer
✔ Bandwidth Monitoring Module (BMM)
✔ Path Selection Module (PSM)
✔ Central Optimization Module (COM)
19
System Architecture-- Communicated Maps
✔ Resource Maps
◆ Cache_map
◆ Edge_map
◆ Comp_map
cache_map
Requests, edge_map, comp_map
20
✔ The SDN controller runs a Central MILP optimization model to respond to the following key questions:
1. Where is the optimal place (i.e., LES, NESs, CSs, or the origin server) in terms of the minimum serving
time and minimum network cost for fetching each client’s requested content quality level from?
2. What is the optimal approach for responding to the requested quality level (i.e., fetch or transcode)?
Optimization Model
21
✔ Considering all feasible actions (nodes and approaches) for serving requests:
Optimization Model-- Action Tree
22
Problem Formulation
Central MILP Optimization Model
Constraints & Objective Function
✓ Resource Map
✓ Requests
✓ Videos Information
✓ Computational Cost
Optimal action for each request
23
Central MILP Optimization Model
● Minimize total serving times (i.e., fetching time plus transcoding time)
● Minimize total network cost (i.e., bandwidth cost plus computational cost)
✔ Multi-Objective Function :
Transmission Time
Transcoding Time
Serving Time
Network Cost
Computational Cost
Bandwidth Cost
24
Central MILP Optimization Model
✔ Constraint Groups :
● Action Selection (AS) constraint
● Serving Time (ST) constraints
● CDN/Origin (CO) constraints
● Resource Consumption (RC) constraints
● Network Cost (NC) constraints
✔ The proposed MILP model is an NP-hard problem
✔ Considering shared links between edge servers to reach other servers changes
the model to a mixed integer non-linear programming (MINLP) model.
25
Local Optimization Model-- Coarse-Grained I (CG)
25
Local Optimization Model-- Coarse-Grained (CG)
✔ Each edge server decides for its associated requests.
COM → LOM
26
Local Optimization Model-- Coarse-Grained (CG)
COM → LOM
✔ The LOM is still suffering from high time complexity
25
27
Fine-Grained I (FG I)-- EFG I
✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request.
LOM → EFG I
28
✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request.
Fine-Grained I (FG I)-- EFG I
✔ What about bandwidth allocation in shared link?
28
✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request.
Fine-Grained I (FG I)-- EFG I
✔ What about bandwidth allocation in shared link?
29
✔ Each edge server
○ runs a lightweight heuristic algorithm upon receiving a request.
○ can inform the SDN controller to run the SDN Fine-Grained (SFG) algorithm to allocate
a new bandwidth value to the other servers.
Fine-Grained II (FG II)-- EFG II
EFG I → EFG II
30
Fine-Grained II (FG II)-- EFG II
31
Fine-Grained II (FG II)-- SFG
Fairness Optimization LP model
32
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.
33
Research Questions (RQs)
✔ How to use edge resources efficiently to optimize users’ QoE and network utilization?
✔ How to design an edge- and SDN-assisted HAS framework for video optimization purposes?
✔ How to establish a collaboration between edge servers to use their potential idle resources
for serving HAS clients.
✔ How to design a network-assisted HAS scheme without client-side modification ?
✔ How we can implement and evaluate proposed approach in a large-scale testbed?
SDNN
F
V
HAS
M
E
C
34
Evaluation Setup
✔ We design a large-scale cloud-based testbed, including 301 nodes (Xen virtual machines):
○ Real network topology Geant and Abilene.
○ 250 DASH clients
○ Four cache servers (Apache server and MongoDB)
○ 40 OpenFlow switches
○ An SDN controller (Floodlight)
○ Five edge servers (each edge server is responsible for 50 clients)
○ A video Dataset including:
■ Fifty video sequences (each video includes 150 segments)
■ 2 seconds segments
■ Three Bitrate ladder
○ BOLA and SQUAD ABR algorithms
○ FFmpeg transcoder
○ Bandwidth monitoring (Floodlight Restful API)
○ LRU cache replacement policy
○ Zipf distribution video access popularity
○ Wondershaper bandwidth allocators
○ Python, Pulp and CPLEX
35
Evaluation Methods
✔ SABR (https://doi.org/10.1145/3083187.3083196):
◆ Non edge-enabled system
◆ Customized DASH players utilize some important resource data (i.e., cache map and bandwidth
map) provided by the SDN controller to make decisions about the next segment requests.
✔ ES-HAS (https://doi.org/10.1145/3458306.3460997):
◆ Non edge-collaborative system
◆ Non transcoding-based system
◆ Each edge runs an MILP model on the collected client requests to serve them via one of actions 1,
7, or 9.
✔ CSDN (10.1109/LCN52139.2021.9524970):
◆ 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 approach I for
◆ each client request to serve it through one of the actions 1, 2, 7, 8 or 9 (Fig. 1).
36
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.
37
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.
38
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.
39
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.
40
Evaluation Results-- Scenario II
✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and
compare the QoE results with state-of-the-art methods:
◆ ASB: Average Segment Bitrate of all downloaded segments.
41
Evaluation Results-- Scenario II
✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and
compare the QoE results with state-of-the-art methods:
◆ AQS: Average Quality Switches, i.e., the number of segments whose bitrate levels change compared to the
previous ones.
42
Evaluation Results-- Scenario II
✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and
compare the QoE results with state-of-the-art methods:
◆ ASD: Average Stall Duration, i.e., the average of total video freeze times in all clients.
◆ ANS: Average Number of Stalls, i.e., the average number of rebuffering events.
43
Evaluation Results-- Scenario II
✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and
compare the QoE results with state-of-the-art methods:
◆ APQ: Average Perceived QoE, calculated by ITU-T Rec. P.1203 mode 0 [1]
[1] https://www.itu.int/net4/ipr/details_ps.aspx?sector=ITU-T&id=P1203-01
44
Evaluation Results-- Scenario III
✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network
utilizations metrics and compare results with other frameworks:
◆ CHR: Cache Hit Ratio, defined as the fraction of segments fetched from the CDN or edge servers.
◆ ETR: Edge Transcoding Ratio, i.e., the fraction of segments transcoded at the edge servers.
45
Evaluation Results-- Scenario III
✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network
utilizations metrics and compare results with other frameworks:
◆ BTL: Backhaul Traffic Load, the volume of segments downloaded from the origin server.
◆ ANU: Average Network Utilization per link, i.e., κl/Kl, where κl and Kl represent the measured traffic (in bit/s)
on link l and the total allocated bandwidth to link l, respectively.
46
Evaluation Results-- Scenario III
✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network
utilizations metrics and compare results with other frameworks:
◆ AST: Average Serving Time for all clients, including fetching time plus transcoding time.
NCV: Network Cost Values, including computational and bandwidth costs.
47
Evaluation Results-- Scenario III
✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network
utilizations metrics and compare results with other frameworks:
◆ ANC: Average Number of Communicated messages from/to the SDN controller to/from all clients (in the
◆ SABR method) or all edge servers (in other frameworks), including OF and HTTP messages.
Conclusion and Future work
● A novel edge-collaborative system for HAS called ARARAT
● ARARAT leverages the 5G/6G paradigms (i.e., SDN, NFV, MEC) to propose a framework for
serving HAS clients with minimum serving latency and networking cost
● We design a multi-layer architecture and formulate the problem as a central
optimization model
● We propose three heuristic approaches to make our framework practical in large-scale
scenarios
● We designed and instantiate a large-scale testbed consisting of 250 clients and
conducts experiments for validating our solutions
● ARARAT approaches outperforms state-of-the-art schemes in terms of users’ QoE,
network cost and the network utilization by at least 47%, 47% and 48%, respectively
Conclusion and Future Work
48
● Augmenting ARARAT with new components to support the following features is some of
our future directions:
○ a RL-based agent
○ CMCD and CMSD communications
○ Multi-stack protocols
○ Auto-configuration nodes
Conclusion and Future Work
49
Thank you for your attention
reza.farahani@aau.at
All rights reserved. ©2020
67

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Collaborative Edge Video Streaming

  • 1. Collaborative Edge-Assisted Systems for HTTP Adaptive Video Streaming Christian Doppler laboratory ATHENA, Institute for Information Technology, Alpen-Adria-Universität Klagenfurt, Austria January 6th , 2023 reza.farahani@aau.at | https://athena.itec.aau.at/ | https://www.rezafarahani.me Reza Farahani
  • 2. Agenda ● Introduction ● Network-Assisted Video Streaming Solutions ● Modern Networking Paradigms ● ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming ● Conclusion
  • 4. ● Video streaming is the predominant today’s Internet traffic. Motivation 1 Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.) [1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport
  • 5. ● Video streaming is the predominant today’s Internet traffic. Motivation 1 Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.) Source: Sandvine GIPR Jan. 2022. [1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport [2] Sandvine, “The Global Internet Phenomena Report”, White Paper, January 2022. [Online]. Available: SandvineReport
  • 6. ● Video streaming is the predominant today’s Internet traffic. Motivation 1 Streaming video service providers’ share of total video traffic in networks (Source: Ericsson Mobility Report Nov. 2022.) Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.) Source: Sandvine GIPR Jan. 2022. [1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport [2] Sandvine, “The Global Internet Phenomena Report”, White Paper, January 2022. [Online]. Available: SandvineReport
  • 7. Video Downloading vs. Streaming 2
  • 8. Video Downloading vs. Streaming 2
  • 11. ● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video streams [1] HTTP Adaptive Video Streaming (HAS) 5 [1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
  • 12. ● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video streams [1] HTTP Adaptive Video Streaming (HAS) 5 [1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
  • 13. ● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video streams [1] HTTP Adaptive Video Streaming (HAS) 5 [1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
  • 14. HTTP Adaptive Video Streaming (HAS) 6 [1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/ Adaptation (ABR) logic is within the client, not normatively specified by a standard, subject to research and development
  • 16. Network-Assisted Video Streaming Solution (NAVS) 7 Network Assistance by HAS Network Assistance for HAS Network Media Server
  • 17. Network-Assisted Video Streaming Solutions (NAVS) 8 SDN NFV MEC Hybrid Systems NAVS Systems 5G/6G Paradigms Content Delivery Networks Emerging Protocols ML/RL supports SFC Computing Continuum Facilities Edge Cloud Fog CMAF QUIC LL-HAS CMCD/SD Transcoding Multi Paradigms Hybrid P2P-CDN Caching Prefetching
  • 18. 9 Our Network-Assisted Solutions ● 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. ● R. Farahani, A. Bentaleb, E. Cetinkaya, C. Timmerer, R. Zimmermann, and H. Hellwagner. Hybrid P2P-CDN Architecture for Live Video Streaming: An Online Learning Approach. IEEE Global Communications Conference (GLOBECOM), 2022. ● 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. ● 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. ● R. Farahani, F. Tashtarian, H. Amirpour, C. Timmerer, M. Ghanbari, and H. Hellwagner. CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming. IEEE 46th Conference on Local Computer Networks (LCN), 2021. ● R. Farahani, F. Tashtarian, A. Erfanian, C. Timmerer, M. Ghanbari, and H. Hellwagner. 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. CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming. The 12th ACM Multimedia Systems Conference (MMSys), 2021. ● 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. IEEE Transactions on Mobile Computing (TMC).
  • 19. 10 ● R. Farahani, A. Bentaleb, C. Timmerer , M. Shojafar, R. Prodan, and H. Hellwagner. SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications. IEEE International Conference on Communications (ICC), 2023. ● R. Farahani, A. Bentaleb, M. Shojafar, C. Timmerer, H. Hellwagner. CP-Steering: CDN- and Protocol-Aware Content Steering Solution for HTTP Adaptive Video Streaming. ACM Mile High Video (MHV), 2023. ● R. Farahani, V. V Menon, P. T Rajendran, H. Hellwagner, M. Ghanbari, C. Timmerer. Transcoding Quality Prediction for Adaptive Video Streaming. ACM Mile High Video (MHV), 2023. ● V. V Menon, R. Farahani, P. T Rajendran, H. Hellwagner, M. Ghanbari, C. Timmerer. Reduced Reference Transcoding Quality Prediction for Video Streaming Applications. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. ● S. Chellappa, R. Farahani, R. Bartos, C. Timmerer, H. Hellwagner. Context-Aware HTTP Adaptive Video Streaming Utilizing QUIC’s Stream Priority. ACM Mile High Video (MHV), 2023. ● A. Bentaleb, R. Farahani, F. Tashtarian, C. Timmerer, H. Hellwagner, R.Zimmermann. Which CDN to Download From? A Client and Server Strategies. ACM Mile High Video (MHV), 2023. ● R. Shokri Kalan, R. Farahani, E. Karsli, C. Timmerer, and H. Hellwagner. Towards Low Latency Live Streaming: Challenges in Real-World Deployment. The 13th ACM Multimedia Systems Conference (MMSys), 2022. ● F. Tashtarian, A. Bentaleb, R. Farahani, M. Nguyen, C. Timmerer, H.Hellwagner, and R. Zimmermann. A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP. IEEE 46th Conference on Local Computer Networks (LCN), 2021. ● A. Erfanian, F. Tashtarian, R. Farahani, C. Timmerer, and H. Hellwagner. On Optimizing Resource Utilization in AVC-based Real-time Video Streaming. The 6th IEEE Conference on Network Softwarization (NetSoft), 2020. Our Network-Assisted Solutions
  • 20. 11 ● This talk covers the following frameworks as Collaborative Edge-Assisted Systems for HTTP Adaptive Video Streaming: 1. LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video (IEEE ICC 2022) 2. ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming (IEEE TNSM 2022) [1] DOI: 10.1109/ICC45855.2022.9838949 [2] DOI: 10.1109/TNSM.2022.3210595 Our Network-Assisted Solutions 5G/6G Paradigms SDN NFV MEC
  • 21. 11 ● This talk covers the following frameworks as Collaborative Edge-Assisted Systems for HTTP Adaptive Video Streaming: ➢ LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video (IEEE ICC 2022) ➢ ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming (IEEE TNSM 2022) ➢ Hybrid P2P-CDN Architecture for Live Video Streaming: An Online Learning Approach (IEEE GLOBECOM 2022) Our Network-Assisted Solutions 5G/6G Paradigms Hybrid Systems SDN NFV MEC P2P NFV CDN MEC
  • 23. ✔ Traditional network architecture: ◆ Complex Network Devices ◆ Management Overhead ◆ Limited Scalability Software-Defined Networking (SDN) 12 Data Plane Control Plane
  • 24. ✔ Conventional network architecture: ◆ 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 Software-Defined Networking (SDN) 12 Source: https://opennetworking.org/sdn-definition/ Data Plane Control Plane
  • 25. ✔ Complementary technology to SDN ✔ Network Functions Virtual Network Functions (VNFs): ◆ run over an open hardware platform ◆ Reduce OpEx, CapEx ◆ accelerate innovations Introduction-Network Function Virtualization (NFV) 13 Router Switch Load Balancer (LB) Firewall Virtualization Layer VRouter VFirewall VSwitch VLB VNF VNF VNF VNF
  • 26. ✔ CDN edge servers: ✔ Multi-access Edge Computing (MEC): ◆ It provides storage and computational resources close to end-users at the network's edge, reducing ● network latency ● bandwidth consumption ◆ Edge servers include limited resources (computational, storage, and bandwidth) Edge Computing 14 MEC server gNodeB Origin server
  • 27. Peer to Peer (P2P) 15 ✔ Alleviate network congestion ✔ Increase streaming stability ✔ Reduce delivery costs Tracker Peers
  • 28. ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming
  • 29. 15 Research Questions (RQs) ✔ How to use edge resources efficiently to optimize users’ QoE and network utilization? ✔ How to design an edge- and SDN-assisted HAS framework for video optimization purposes? ✔ How to establish a collaboration between edge servers to use their potential idle resources for serving HAS clients. ✔ How to design a network-assisted HAS scheme without client-side modification ? ✔ How we can implement and evaluate proposed approach in a large-scale testbed? SDNN F V HAS M E C
  • 30. 16 System Architecture-- Edge Layer Edge Layer ✔ Edge Servers: ◆ Local Edge Server (LES) ◆ Neighboring Edge Server (NES) ✔ Edge Functions: ◆ Partial Cache (PC) ◆ Video Transcoder (Tran.) MEC NFV
  • 31. 17 System Architecture-- CDN/Origin Layer ✔ Multi-CDN Servers ✔ Origin Server
  • 32. 18 System Architecture-- Control Layer ✔ Bandwidth Monitoring Module (BMM) ✔ Path Selection Module (PSM) ✔ Central Optimization Module (COM)
  • 33. 19 System Architecture-- Communicated Maps ✔ Resource Maps ◆ Cache_map ◆ Edge_map ◆ Comp_map cache_map Requests, edge_map, comp_map
  • 34. 20 ✔ The SDN controller runs a Central MILP optimization model to respond to the following key questions: 1. Where is the optimal place (i.e., LES, NESs, CSs, or the origin server) in terms of the minimum serving time and minimum network cost for fetching each client’s requested content quality level from? 2. What is the optimal approach for responding to the requested quality level (i.e., fetch or transcode)? Optimization Model
  • 35. 21 ✔ Considering all feasible actions (nodes and approaches) for serving requests: Optimization Model-- Action Tree
  • 36. 22 Problem Formulation Central MILP Optimization Model Constraints & Objective Function ✓ Resource Map ✓ Requests ✓ Videos Information ✓ Computational Cost Optimal action for each request
  • 37. 23 Central MILP Optimization Model ● Minimize total serving times (i.e., fetching time plus transcoding time) ● Minimize total network cost (i.e., bandwidth cost plus computational cost) ✔ Multi-Objective Function : Transmission Time Transcoding Time Serving Time Network Cost Computational Cost Bandwidth Cost
  • 38. 24 Central MILP Optimization Model ✔ Constraint Groups : ● Action Selection (AS) constraint ● Serving Time (ST) constraints ● CDN/Origin (CO) constraints ● Resource Consumption (RC) constraints ● Network Cost (NC) constraints ✔ The proposed MILP model is an NP-hard problem ✔ Considering shared links between edge servers to reach other servers changes the model to a mixed integer non-linear programming (MINLP) model.
  • 39. 25 Local Optimization Model-- Coarse-Grained I (CG)
  • 40. 25 Local Optimization Model-- Coarse-Grained (CG) ✔ Each edge server decides for its associated requests. COM → LOM
  • 41. 26 Local Optimization Model-- Coarse-Grained (CG) COM → LOM ✔ The LOM is still suffering from high time complexity 25
  • 42. 27 Fine-Grained I (FG I)-- EFG I ✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request. LOM → EFG I
  • 43. 28 ✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request. Fine-Grained I (FG I)-- EFG I ✔ What about bandwidth allocation in shared link?
  • 44. 28 ✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request. Fine-Grained I (FG I)-- EFG I ✔ What about bandwidth allocation in shared link?
  • 45. 29 ✔ Each edge server ○ runs a lightweight heuristic algorithm upon receiving a request. ○ can inform the SDN controller to run the SDN Fine-Grained (SFG) algorithm to allocate a new bandwidth value to the other servers. Fine-Grained II (FG II)-- EFG II EFG I → EFG II
  • 46. 30 Fine-Grained II (FG II)-- EFG II
  • 47. 31 Fine-Grained II (FG II)-- SFG Fairness Optimization LP model
  • 48. 32 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.
  • 49. 33 Research Questions (RQs) ✔ How to use edge resources efficiently to optimize users’ QoE and network utilization? ✔ How to design an edge- and SDN-assisted HAS framework for video optimization purposes? ✔ How to establish a collaboration between edge servers to use their potential idle resources for serving HAS clients. ✔ How to design a network-assisted HAS scheme without client-side modification ? ✔ How we can implement and evaluate proposed approach in a large-scale testbed? SDNN F V HAS M E C
  • 50. 34 Evaluation Setup ✔ We design a large-scale cloud-based testbed, including 301 nodes (Xen virtual machines): ○ Real network topology Geant and Abilene. ○ 250 DASH clients ○ Four cache servers (Apache server and MongoDB) ○ 40 OpenFlow switches ○ An SDN controller (Floodlight) ○ Five edge servers (each edge server is responsible for 50 clients) ○ A video Dataset including: ■ Fifty video sequences (each video includes 150 segments) ■ 2 seconds segments ■ Three Bitrate ladder ○ BOLA and SQUAD ABR algorithms ○ FFmpeg transcoder ○ Bandwidth monitoring (Floodlight Restful API) ○ LRU cache replacement policy ○ Zipf distribution video access popularity ○ Wondershaper bandwidth allocators ○ Python, Pulp and CPLEX
  • 51. 35 Evaluation Methods ✔ SABR (https://doi.org/10.1145/3083187.3083196): ◆ Non edge-enabled system ◆ Customized DASH players utilize some important resource data (i.e., cache map and bandwidth map) provided by the SDN controller to make decisions about the next segment requests. ✔ ES-HAS (https://doi.org/10.1145/3458306.3460997): ◆ Non edge-collaborative system ◆ Non transcoding-based system ◆ Each edge runs an MILP model on the collected client requests to serve them via one of actions 1, 7, or 9. ✔ CSDN (10.1109/LCN52139.2021.9524970): ◆ 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 approach I for ◆ each client request to serve it through one of the actions 1, 2, 7, 8 or 9 (Fig. 1).
  • 52. 36 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.
  • 53. 37 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.
  • 54. 38 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.
  • 55. 39 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.
  • 56. 40 Evaluation Results-- Scenario II ✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and compare the QoE results with state-of-the-art methods: ◆ ASB: Average Segment Bitrate of all downloaded segments.
  • 57. 41 Evaluation Results-- Scenario II ✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and compare the QoE results with state-of-the-art methods: ◆ AQS: Average Quality Switches, i.e., the number of segments whose bitrate levels change compared to the previous ones.
  • 58. 42 Evaluation Results-- Scenario II ✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and compare the QoE results with state-of-the-art methods: ◆ ASD: Average Stall Duration, i.e., the average of total video freeze times in all clients. ◆ ANS: Average Number of Stalls, i.e., the average number of rebuffering events.
  • 59. 43 Evaluation Results-- Scenario II ✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and compare the QoE results with state-of-the-art methods: ◆ APQ: Average Perceived QoE, calculated by ITU-T Rec. P.1203 mode 0 [1] [1] https://www.itu.int/net4/ipr/details_ps.aspx?sector=ITU-T&id=P1203-01
  • 60. 44 Evaluation Results-- Scenario III ✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network utilizations metrics and compare results with other frameworks: ◆ CHR: Cache Hit Ratio, defined as the fraction of segments fetched from the CDN or edge servers. ◆ ETR: Edge Transcoding Ratio, i.e., the fraction of segments transcoded at the edge servers.
  • 61. 45 Evaluation Results-- Scenario III ✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network utilizations metrics and compare results with other frameworks: ◆ BTL: Backhaul Traffic Load, the volume of segments downloaded from the origin server. ◆ ANU: Average Network Utilization per link, i.e., κl/Kl, where κl and Kl represent the measured traffic (in bit/s) on link l and the total allocated bandwidth to link l, respectively.
  • 62. 46 Evaluation Results-- Scenario III ✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network utilizations metrics and compare results with other frameworks: ◆ AST: Average Serving Time for all clients, including fetching time plus transcoding time. NCV: Network Cost Values, including computational and bandwidth costs.
  • 63. 47 Evaluation Results-- Scenario III ✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network utilizations metrics and compare results with other frameworks: ◆ ANC: Average Number of Communicated messages from/to the SDN controller to/from all clients (in the ◆ SABR method) or all edge servers (in other frameworks), including OF and HTTP messages.
  • 65. ● A novel edge-collaborative system for HAS called ARARAT ● ARARAT leverages the 5G/6G paradigms (i.e., SDN, NFV, MEC) to propose a framework for serving HAS clients with minimum serving latency and networking cost ● We design a multi-layer architecture and formulate the problem as a central optimization model ● We propose three heuristic approaches to make our framework practical in large-scale scenarios ● We designed and instantiate a large-scale testbed consisting of 250 clients and conducts experiments for validating our solutions ● ARARAT approaches outperforms state-of-the-art schemes in terms of users’ QoE, network cost and the network utilization by at least 47%, 47% and 48%, respectively Conclusion and Future Work 48
  • 66. ● Augmenting ARARAT with new components to support the following features is some of our future directions: ○ a RL-based agent ○ CMCD and CMSD communications ○ Multi-stack protocols ○ Auto-configuration nodes Conclusion and Future Work 49
  • 67. Thank you for your attention reza.farahani@aau.at All rights reserved. ©2020 67