Adaptive Video Streaming in Software-Defined Mobile Networks: A Deep Reinforcement Learning Approach

Luo, Jianshu; Yu, F. Richard; Chen, Qianbin; Tang, Lun; Zhang, Zhicai

doi:10.1109/globecom38437.2019.9013634

Cited by 4 publications

(1 citation statement)

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“…Due to the more dynamic and complex nature of the problem, advanced techniques based on DL are being proposed. For instance, in [176], the joint caching, transcoding and transmission of videos is formulated as Markov Decision Process (MDP) and solved using Deep Reinforcement Learning (DRL) to improve user experience in adaptive video streaming. In [177], authors consider joint optimization of caching, processing and radio resources to maximize system revenue.…”

Section: ) Joint Radio Caching and Processingmentioning

confidence: 99%

A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

et al. 2022

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5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowdsourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-toend communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultrareliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-ofthe-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.

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Section: ) Joint Radio Caching and Processingmentioning

confidence: 99%