RICHTER is a hybrid P2P-CDN architecture for low latency live video streaming that employs virtualized edge servers. It addresses challenges in CDN- and HAS-based streaming by leveraging characteristics of P2P networks and CDN systems. RICHTER utilizes peers' resources through a distributed transcoding approach in addition to video transmission. Virtual tracker servers located near base stations direct clients' requests and respond based on fetching content from peers, edge servers, CDN servers or origin server depending on latency. An optimization problem and heuristic approach are proposed to guide system operation and answer research questions on optimal placement, response approach, sufficient resources and seeder replacement.

1. RICHTER: Hybrid P2P-CDN Architecture for Low Latency Live Video Streaming
Reza Farahani1
, Hadi Amirpour 1
, Farzad Tashtarian1
, Abdelhak Bentaleb 2
, Christian Timmerer 1
, Hermann Hellwagner 1
, and Roger Zimmerman 2
1
Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität, Klagenfurt, Austria
2
School of Computing, National University of Singapore, Singapore
Introduction
Content Delivery Network (CDN) and HTTP Adaptive Streaming (HAS) are considered
the principal video delivery technologies over the Internet.
Challenge in CDN- and HAS-based video streaming
Despite the wide usage of CDN and HAS technologies, designing
cost‐effective
scalable
flexible
architectures that support low latency and high quality live video streaming is still
a challenge.
To address this issue, we leverage existing works that have combined the character‐
istics of Peer‐to‐Peer (P2P) networks and CDN‐based systems and introduce a hybrid
CDN‐P2P live streaming architecture.
Hybrid CDN-P2P architectures
When dealing with the technical complexity of managing hundreds or thousands
of concurrent streams, such hybrid systems can provide
low latency
high quality
through enabling the delivery architecture to switch between the CDN and the
P2P modes.
However, modern networking paradigms have not been extensively employed to
design such systems.
Contributions
We employ modern networking paradigms such as
Edge Computing
Network Function Virtualization (NFV)
Distributed video transcoding
to introduce a hybRId P2P‐CDN arcHiTecture for low LatEncy live video
stReaming (RICHTER).
RICHTER employs virtualized edge servers to serves clients’ live video stream re‐
quests through
fetching them from P2P network or CDN, or edge servers.
transcoding them from higher qualities at the best peer or the edge server.
to obtain the lowest latency values.
RICHTER Architecture
Live video
upload
L
L
L
L
CDN Network
Live video
upload
Virtual Tracker Server (VTS)
L
Seeders
Leechers
P2P Network
Peer Transcoder
Edge Transcoder
gNodeB
CDN
Server
VTS
(PC)
Origin
Server
Peer
(Tran.)
VTS
(Tran.)
VTS
(Tran.)
2 4
3 5 6 7
Action Tree
Clients
1
RICHTER Contribution
Partial Cache (PC)
Peer
Figure 1. RICHTER architecture
We propose RICHTER architecture consisting of three core components:
CDN network
The CDN network is constructed by multiple CDN servers and an origin servers:
CDN servers contain various parts of video sequences.
Origin server contains all video segments in multiple representations.
RICHTER fetches requested qualities or higher qualities (for running trascoding
function) from them to serve clients’ request.
P2P network
The P2P network includes two types of peers:
Seeders’ requests can be served by CDN servers, the origin server, and VTSs.
Leechers’requests can be served by adjacent peers and VTSs.
Given the continuous increases in smartphone capabilities, e.g.,
high broadband bandwidth access to the Internet
energy resources
hardware‐accelerated video transcoding
RICHTER utilizes the peers’ resources to provide a distributed video transcoding
approach besides video transmission.
Virtual Tracker Servers (VTS)
VTS servers are located close to base stations (gNodeB) and equipted with
Partial cache to serve popular clients’ requests immediately.
Transcoding function to serve clients’ requests from existing higher qualities
directly.
The clients’ live video stream requests are directed to the VTSs, and then they are
answered based on the VTSs decisions.
Proposed Approach
The following research questions will be taken into account by RICHTER.
Research questions
1. Where is the optimal place (i.e., adjacent peers, VTS, CDN servers, or origin
server) in terms of lowest latency for fetching each client’s requested content
quality level from, while efficiently utilizing the available resources?
2. What is the optimal approach for responding to the requested quality level (i.e.,
fetch or transcode)
3. How many seeders and VTSs are sufficient to serve the leechers?
4. How to replace seeders when one of them leaves the system?
To answering the aforementioned research questions, VTS servers
track associated clients’ requests and store a mapping between all transmitted
videos and all served clients in its peer‐map list.
monitor the system frequently to obtain precise information about the available
bandwidth to reach each CDN server and peers’ available resources.
Therefore, when a VTS receives a new request, it can find the optimal solution (i.e.,
in terms of minimum latency) from the Action Tree (Fig. 1)
All Feasible Actions
1. Use the P2P network and transmit the requested quality directly from the best
adjacent peer (action 1 in the action tree).
2. Transcode the requested quality from a higher quality at the best adjacent peer
and transmit it through the P2P network.
3. Fetch the requested quality directly from the edge, i.e., the VTS.
4. Transcode the requested quality from a higher quality at the VTS.
5. Fetch the requested quality from the origin server.
6. Fetch a higher quality from the best CDN server and transcode it at the VTS.
7. Fetch the requested quality from the best CDN server.
Future Plan
The next steps of this paper are listed as follows.
1. We model the adaptive live low latency streaming as an optimization problem to
guide the system operation according to RICHTER’s action tree (Fig. 1), and
answer the aforementioned questions.
2. We propose a near‐optimal heuristic approach with minimum overhead that
could be run practically on the proposed VTSs.
3. Finally, this approach is validated through experiments on a real‐world testbed,
including hundreds of clients, and its performance is compared to other
state‐of‐the‐art approaches.
https:/
/www.athena.itec.aau.at Mile High Video (MHV) 2022, Denver, USA reza.farahani@aau.at