Lecture: EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
Master data
Title: | EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming |
Description: | Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements. |
Keywords: | Edge Computing, HTTP Adaptive Streaming, Network-assisted Video Streaming, Quality of Experience |
Type: | Registered lecture |
Homepage: | https://ieeexplore.ieee.org/document/9524883 |
Event: | The 46th IEEE Conference on Local Computer Networks (LCN 2021) (Edmonton) |
Date: | 07.10.2021 |
lecture status: | stattgefunden (online) |
Participants
Jesus Aguilar Armijo (internal) |
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Christian Timmerer (internal) |
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Hermann Hellwagner (internal) |
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Assignment
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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