EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

Aguilar-Armijo, Jesus; Timmerer, Christian; Hellwagner, Hermann

doi:10.1109/lcn52139.2021.9524883

Cited by 9 publications

(4 citation statements)

References 14 publications

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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%

“…Aguilar-Armijo et al [2] propose EADAS, an edge-based mechanism consisting of (i) an adaptation algorithm and (ii) a segment prefetching scheme that supports the client-based ABR algorithm by improving its decisions on-the-fly. EADAS leverages edge capabilities such as the availability of player metrics, radio metrics and all clients' requests, as well as storage and computing power to improve the final QoE and fairness of the video streaming clients, outperforming other ABR solutions.…”

Section: Related Workmentioning

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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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Aguilar-Armijo,

Çetinkaya,

Timmerer

et al. 2022

Preprint

Self Cite

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“…This causes scalability problems in the adaptive streaming that handles multiple clients simultaneously. Thus, schemes moving adaptation intelligence to the mobile edge have emerged for efficient multi-client adaptive streaming [ 23 , 24 , 25 , 26 ]. In the middle of the server and the clients, the mobile edge collects the information of clients and the specific network condition, such as the channel status.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Based Adaptive Streaming Scheme with Edge Computing Assistance

Kim

Chung

2022

Sensors

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Dynamic Adaptive Streaming over HTTP (DASH) is a promising scheme for improving the Quality of Experience (QoE) of users in video streaming. However, the existing schemes do not perform coordination among clients and depend on fixed heuristics. In this paper, we propose an adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme improves the overall QoE of clients and QoE fairness among clients based on a state-of-the-art reinforcement learning algorithm. Edge computing assistance plays a role in providing client-side observations to the mobile edge, making agents utilize this information when generating a policy for multi-client adaptive streaming. We evaluated the proposed scheme through simulation-based experiments under various network conditions. The experimental results show that the proposed scheme achieves better performance than the existing schemes.

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“…A novel approach is edge computer-based algorithms [21][22][23][24] which are used only in the case of cell phone networks. Implementing such an algorithm requires reserving a vast amount of physical resources.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2023

Adv. Electr. Comp. Eng.

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Video streaming takes the largest share of internet traffic today, and MPEG dynamic adaptive streaming over HTTP (DASH) has become dominant among other video streaming standards and protocols. According to the DASH standard, multimedia content is encoded in different quality levels with different bitrates located on the server, and users can request multimedia content of any available bitrate. The user side determines the desired bitrate in the unit called adaptation bitrate (ABR) logic. Many ABR algorithms have been proposed to improve the quality of experience (QoE). The main criteria for determining QoE are average bitrate, number of switches between resolutions, and number of buffer underflows. This paper presents a hybrid DASH adaptation algorithm that uses the following input values: current buffer occupancy level, network throughput value calculated on the last downloaded DASH segment, and Takagi-Sugeno-Kang model output that represents expected throughput in the next segment download iteration. We compared the proposed algorithm with several other algorithms and the results show that it outperforms others in average bitrate and number of bitrate switches. Furthermore, our algorithm prevented all buffer underflows.

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EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

Cited by 9 publications

References 14 publications

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

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

Reinforcement Learning-Based Adaptive Streaming Scheme with Edge Computing Assistance

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