NEWCAST: Joint Resource Management and QoE-Driven Optimization for Mobile Video Streaming

Triki, Imen; El-Azouzi, Rachid; Haddad, Majed

doi:10.1109/tnsm.2019.2952498

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…1) Improving QoE: In [17], the authors propose to improve the QoE of video streaming through DASH-aware bandwidth allocation. [18], [19] use client-server cooperation to select video streaming bitrates and improve the QoE. In contrast, our work assumes that no server or Base Station (BS) side information is available at the client.…”

Section: B Related Workmentioning

confidence: 99%

Improving UE Energy Efficiency Through Network-Aware Video Streaming Over 5G

Palit

Sen

Mondal³

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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Adaptive Bitrate (ABR) Streaming over the cellular networks has been well studied in the literature; however, existing ABR algorithms primarily focus on improving the end-users' Quality of Experience (QoE) while ignoring the resource consumption aspect of the underlying device. Consequently, proactive attempts to download video data to maintain the user's QoE often impact the battery life of the underlying device unless the download attempts are synchronized with the network's channel condition. In this work, we develop EnDASH-5G -a wrapper over the popular DASH-based ABR streaming algorithm, which establishes this synchronization by utilizing a network-aware video data download mechanism. EnDASH-5G utilizes a novel throughput prediction mechanism for 5G mmWave networks by upgrading the existing throughput prediction models with a transfer learning-based approach leveraging publicly available 5G datasets. It then exploits deep reinforcement learning to dynamically decide the playback buffer length and the video bitrate using the predicted throughput. This ensures that the data download attempts get synchronized with the underlying network condition, thus saving the device's battery power. From a thorough evaluation of EnDASH-5G, we observe that it achieves a near 30.5% decrease in the maximum energy consumption than the state-of-the-art Pensieve ABR algorithm while performing almost at par in QoE.

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Section: B Related Workmentioning

confidence: 99%

Improving UE Energy Efficiency Through Network-Aware Video Streaming Over 5G

Palit

Sen

Mondal³

et al. 2023

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

show abstract

“…The recent ETSI technical specification [17] on server-and network-assisted DASH (SAND) introduces network elements with partial knowledge of the ongoing streaming session and of the client's QoE metrics [18]. Leveraging SAND, multiuser optimization can be addressed [19], [20]. Besides, ABR can be carried out at the edge network, by multi-coordinators and multi-server frameworks [21], [22], on top of video transcoders [23] or proportional fair schedulers supporting premium users [24].…”

Section: Introductionmentioning

confidence: 99%

Premium quality or guaranteed fluidity? Client-transparent DASH-aware bandwidth allocation at the radio access network

et al. 2022

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In this paper we propose a novel client-transparent Dynamic Adaptive Streaming over HTTP (DASH) -aware bandwidth allocation strategy. The approach, while being application layer transparent, guarantees fluidity to all users but it provides priority-based services to premium users, identified by their Willingness-To-Pay (WTP) profiles. Since different service qualities can be accommodated using WTP, the approach can be extended to XR services, immersive videos, live uplink streaming. To achieve this goal, the allocation problem is firstly formulated as a classical Game Theory problem whose closed form solution is clearly understood in the literature. In a nutshell, the WTPbased, Game Theoretically Optimal Bandwidth Allocation (WTP-GTOBA), firstly satisfies the minimum bandwidth needs of each user and then fairly distributes the residual bandwidth. WTP-GTOBA can be approximately implemented by a greedy, application layer transparent algorithm to be implemented at a DASHaware network element managing different neighbouring radio access stations. Thereby, the proposed method is suitable for integration of WTP and resource management among multiple service providers and heterogeneous client groups. Numerical simulations carried out under a realistic scenario show that the proposed approach outperforms state-of-the-art applicationlayer transparent competitors, providing premium quality and/or guaranteed fluidity to different users based on their WTP.

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A social-aware video sharing solution using demand prediction of epidemic-based propagation in wireless networks

Jia,

Cui,

et al. 2023

Multimedia Systems

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The video services that account for the majority of global network traffic consume significant amounts of electricity and network resources to meet the large-scale demand of users. Variations in user interest and social influence lead to high maintenance costs for achieving a dynamic balance between supply and demand, which negatively impacts the sustainable development of video services. In this paper, we propose a social-aware video-sharing solution using demand prediction of epidemic-based propagation in wireless networks (SDPEP). SDPEP constructs a video propagation model based on user “pull” and “push” sharing behaviors and designs an estimation method for calculating the probability of video fetching by investigating user interests and social relationships. SDPEP uses the probability of video fetching to calculate the basic reproduction number during epidemic-based video propagation, predicting user demand during the propagation process. To ensure efficient caching with low-cost adjustments to video distribution, SDPEP employs a caching-based adjustment strategy for distributing videos while maintaining dynamic balance between supply and demand. Extensive testing shows that SDPEP outperforms other state-of-the-art solutions.

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NEWCAST: Joint Resource Management and QoE-Driven Optimization for Mobile Video Streaming

Abstract: The associate editor coordinating the review of this article and approving it for publication was D. Pezaros.

Cited by 5 publications

References 35 publications

Improving UE Energy Efficiency Through Network-Aware Video Streaming Over 5G

Improving UE Energy Efficiency Through Network-Aware Video Streaming Over 5G

Premium quality or guaranteed fluidity? Client-transparent DASH-aware bandwidth allocation at the radio access network

A social-aware video sharing solution using demand prediction of epidemic-based propagation in wireless networks

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