Stammdaten

Titel: LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
Beschreibung:

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.

Schlagworte: 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
Typ: Angemeldeter Vortrag
Homepage: https://icc2022.ieee-icc.org/
Veranstaltung: IEEE International Conference on Communications (IEEE ICC 2022) (Seoul, Südkorea)
Datum: 17.05.2022
Vortragsstatus: stattgefunden (online)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Österreich
   martina.steinbacher@aau.at
http://itec.aau.at/
zur Organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 1020 - Informatik
Forschungscluster Kein Forschungscluster ausgewählt
Vortragsfokus
  • Science to Science (Qualitätsindikator: II)
Klassifikationsraster der zugeordneten Organisationseinheiten:
TeilnehmerInnenkreis
  • Überwiegend international
Publiziert?
  • Ja
Arbeitsgruppen
  • Multimedia Communication

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