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EADAS: Edge Assisted Adaptation
Scheme for HTTP Adaptive Streaming
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IEEE 46th Conference on Local Computer Networks (LCN)
October 4-7, 2021
Jesús Aguilar Armijo, Christian Timmerer, and Hermann Hellwagner
Christian Doppler laboratory ATHENA | Alpen-Adria-Universität Klagenfurt | Austria
jesus.aguilar@aau.at | https://athena.itec.aau.at/

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● Introduction
● Algorithm
● Segment prefetching
● Clustering per subscription
● Results
● Q & A
Table of
content
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● Client-based algorithm has limited information available to perform
its decisions
● Usually, edge-based ABR algorithms are based on an optimization
model with time-slots, where they collect all the requests from the
users
○ But requests are not synchronized and they might have
different segment duration
● We propose EADAS, an edge-based scheme that supports the
client-based ABR algorithm, improving its adaptation decisions
● Provide awareness of other users requests, segment prefetching
support and different level of subscription
● Operates in an on-the-fly manner with minimum latency added. It is
lightweight in contrast to optimization-based, state-of-the-art
time-slotted approaches.
Introduction
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● EADAS algorithm is executed for each segment request
● It focus on improve QoE and fairness among the users
● The 𝛼 value in our algorithm can prioritize QoE or fairness according to our preferences
● Lower 𝛼 values prioritize fairness, higher alpha values prioritize QoE:
final score = 𝛼 x quality score + (1 - 𝛼 ) x fairness score
EADAS algorithm
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● We study different segment prefetching policies, analyzing costs and benefits
○ Last segment quality (LSQ)
○ Last segment quality plus (LSQ+)
○ All segment qualities (ASQ)
● We test SARA ABR algorithm with different prefetching policies
● Results show that throughput-based or hybrid ABR algorithms are not prepared to support segment
prefetching, we have radio throughput miscalculations
● EADAS was designed to support segment prefetching and leverage its benefits
EADAS segment prefetching
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● Service providers may want to offer different levels of subscriptions to offer several pricing schemes
(e.g., basic, premium) to customers with differentiated services, e.g., in terms of QoE
● For example, premium clients may benefit from better segment prefetching policies
● EADAS algorithm can group users with the same characteristics and assure fairness among them
● We conduct experiment with and without EADAS, with half of the clients assign to be premium with
segment prefetching LSQ+:
● Results show how EADAS clustering per subscription increase the premium user QoE a 26% (from
3.35 to 4.22) and the basic user QoE a 20% (from 3.45 to 4.14)
● EADAS also increases the fairness among users of the same cluster
EADAS clustering per subscription
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● As EADAS aims to improve client-based ABR algorithms, we test our mechanism using real 4G radio
traces using three client-based ABR algorithms with different approaches:
○ Throughput-based ABR (TBA)
○ Buffer-based ABR (BBA)
○ Hybrid-based ABR (SARA)
● EADAS improves the performance of the three ABR algorithms, improving the mean bitrate and/or
reducing the number of stalls
● EADAS improves the QoE by 4.6%, 23.5%, and 24.4% and the mean fairness index by 11%, 3.4% and
5.8% for BBA, TBA, and SARA, respectively
EADAS results
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Thank you
Q&A
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