Master data

Title: LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
Description:

With the emerging demands of high-definition and low-latency video streams, HTTP Adaptive Streaming (HAS) is considered the principal video delivery technology over the Internet. Network-assisted video streaming schemes, which employ modern networking paradigms, e.g., Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing, have been introduced as promising complementary solutions in the HAS context to improve users’ Quality of Experience (QoE) as well as network utilization. However, the existing network-assisted HAS schemes have not fully used edge collaboration techniques and SDN capabilities for achieving the aforementioned aims. To bridge this gap, this paper introduces a coLlaborative Edge- and SDN-Assisted framework for HTTP aDaptive vidEo stReaming (LEADER). In LEADER, the SDN controller collects various information items and runs a central optimization model that minimizes the HAS clients’ serving time, subject to the network’s and edge servers’ resource constraints. Due to the NP-completeness and impractical overheads of the central optimization model, we propose an online distributed lightweight heuristic approach consisting of two phases that runs on the SDN controller and edge servers, respectively. We implement the proposed framework, conduct our experiments on a large-scale testbed including 250 HAS players, and compare its effectiveness with other strategies. The experimental results demonstrate that LEADER outperforms baseline schemes in terms of both users’ QoE and network utilization, by at least 22% and 13%, respectively.

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Network-Assisted Video Streaming, Video Transcoding, Quality of Experience (QoE), Software-Defined Networking (SDN), Network Function Virtualization (NFV), Edge Computing, Edge Collaboration
Type: Registered lecture
Homepage: https://icc2022.ieee-icc.org/
Event: IEEE International Conference on Communications (IEEE ICC 2022) (Seoul, Südkorea)
Date: 17.05.2022
lecture status: stattgefunden (online)

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Austria
   martina.steinbacher@aau.at
http://itec.aau.at/
To organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation

Subject areas
  • 1020 - Computer Sciences
Research Cluster No research Research Cluster selected
Focus of lecture
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
Group of participants
  • Mainly international
Published?
  • Yes
working groups
  • Multimedia Communication

Cooperations

No partner organisations selected