Edge Computing Assisted Adaptive Streaming Scheme for Mobile Networks

Kim, Minsu; Chung, Kwangsue

doi:10.1109/access.2020.3047373

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…Results show that their mechanism outperforms similar works such as the mechanism presented in [8] and Prius [7]. All these different edge computingassisted solutions for adaptive video streaming [7]- [9] use an optimization model. These methods collect the segment requests for all the video streaming clients during a certain time (i.e., a time slot) and then optimize the final decisions.…”

Section: Related Workmentioning

confidence: 88%

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EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

Aguilar-Armijo¹,

Timmerer²,

Hellwagner³

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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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-thefly 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.

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Section: Related Workmentioning

confidence: 88%

“…Kim et al [9] propose an edge computing-assisted adaptive streaming scheme for mobile networks. It provides an optimization model that takes into account fairness, QoE, and resource utilization.…”

Section: Related Workmentioning

confidence: 99%

EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

Aguilar-Armijo¹,

Timmerer²,

Hellwagner³

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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“…To compare performance, we implement three client-based ABR algorithms that follow three different approaches: throughput-based ABR (TBA [19]), bufferbased ABR (BBA [12]), and hybrid-based ABR (SARA [14]). Moreover, we implement three edge-based ABR algorithms: Greedy-Based Bitrate Allocation (GBBA) [15], EADAS [2], and our proposed scheme ECAS-ML.…”

Section: Methodsmentioning

confidence: 99%

“…For the radio traces, our simulator uses real radio traces from a 4G dataset [22] with different mobility patterns. This dataset consists of 5 trace categories, and each category contains a different number of radio traces (i.e., bus (16), car (53), pedestrian (31), static (15), and train ( 20)). There are 135 radio traces in total, and we removed 6 from the dataset since they were causing an imbalance in the dataset due to being too long.…”

Section: Methodsmentioning

confidence: 99%

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ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming

Aguilar-Armijo,

Çetinkaya,

Timmerer

et al. 2022

Preprint

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As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.

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