Intelligent aerial video streaming: Achievements and challenges

Nguyen, The-Vinh; Nguyen, Ngoc Phi; Kim, Cheonshik; Dao, Nhu‐Ngoc

doi:10.1016/j.jnca.2022.103564

Cited by 13 publications

(8 citation statements)

References 107 publications

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“…Deep reinforcement learning overcomes the limitations of reinforcement learning and uses deep neural networks to approximate value functions, effectively improving the learning speed and performance of reinforcement learning [21]. A reinforcement learning algorithm has been widely used [22,23]. To address the worse performance in the multiagent environment, QMIX adopts the framework of centralized learning and distributed execution, but the characteristic limits its applicability to cooperative scenarios [24].…”

Section: Multiagent Deep Reinforcement Learningmentioning

confidence: 99%

Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution

Tang

Chen

2023

Future Internet

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Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user quality of experience (QoE). Even while edge servers can offer near-end services to local users, it is challenging to accommodate a high number of mobile users in a dynamic environment due to their restricted capacity to maximize user long-term QoE. We are motivated to integrate user allocation and bitrate adaptation into one optimization objective and propose a multiagent reinforcement learning method combined with an attention mechanism to solve the problem of multiedge servers cooperatively serving users. Through comparative experiments, we demonstrate the superiority of our proposed solution in various network configurations. To tackle the edge user allocation problem, we proposed a method called attention-based multiagent reinforcement learning (AMARL), which optimized the problem in two directions, i.e., maximizing the QoE of users and minimizing the number of leased edge servers. The performance of AMARL is proved by experiments.

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Section: Multiagent Deep Reinforcement Learningmentioning

confidence: 99%

Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution

Tang

Chen

2023

Future Internet

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“…9770/jesi.2023.11.2(9) prior content downloading and retention, with supported formats encompassing auditory and visual media, podcasts and webcasts, musical vignettes, cinematic productions, and serialized television programming (Frade et al, 2021;Austerberry, 2013). As the tendency for this mode of amusement increases, streaming services have emerged as a paramount facet of internet consumption (Lambrinos & Demetriou, 2010;Bampis & Bovik, 2018;Laterman et al, 2017;Nguyen et al, 2023).…”

Section: Entrepreneurship and Sustainability Issuesmentioning

confidence: 99%

“…The seamless integration of these elements mandates steadfast, high-velocity Internet connectivity. Diverse devices of varying capabilities and specifications align with an array of personalized requisites, including but not limited to high-resolution 4K playback mechanisms, hands-free contrivances, specialized gaming peripherals, devices furnished with or bereft of remote-control interfaces, as well as economically viable options (Lambrinos & Demetriou, 2010;Bampis & Bovik, 2018;Nguyen et al, 2023).…”

Section: Review Of Literature Innovative Technology For Entertainmentmentioning

confidence: 99%

Peculiarities of video streaming industry development

Ioanid,

Iliescu

2023

JESI

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The term "streaming" defines modern technologies for distributing the contents of files and audio or video in a continuous stream via a wired or wireless internet connection. The word "streaming" also means a "continuous flow" and represents a newly developed multimedia technology content carried out between the transmitting source and the receiving device. This article delves into the multifaceted dimensions of the streaming services industry, emphasizing the pivotal role played by innovation, technology, and performance. Through a comprehensive exploration, we highlight the transformative impact of real-time content. With projections indicating continued exponential growth, the industry's trajectory is analyzed within the context of the multidimensional triadic model. Case studies on industry leaders underscore the criticality of research and development investments in driving profitability. As the industry evolves, companies prioritizing user experience and technological innovation prepare for sustained success in this dynamic landscape. This article is a compass for industry stakeholders, providing insights into the intricate interplay of innovation, technology, and performance within the streaming services sector.

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“…The contemporary landscape of the entertainment industry is in the throes of a profound metamorphosis, propelled by the inexorable convergence of technological prowess and an insatiable consumer appetite for ondemand content developed in recent years (Lambrinos & Demetriou, 2010;Bampis & Bovik, 2018;Sliwa et al, 2021). Central to this paradigm shift is the ascendant dominion of streaming platforms, which have swiftly ascended to the vanguard of content consumption preferences and are leading the entertainment industry (Nguyen et al, 2023). Within this dynamic milieu, the perspective of transformation resides in the rapid evolution of streaming technologies, fundamentally redefining the contours of new product development strategies within the entertainment sector.…”

Section: Introductionmentioning

confidence: 99%

Streaming ahead: technological innovations redefining new product development in the entertainment industry

Iliescu,

Ioanid

2024

JESI

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This article delves into the dynamic intersection of technology and innovation within the streaming industry, offering a comprehensive analysis of the evolving landscape of content creation, delivery, and consumption. By scrutinizing the integration of cutting-edge technologies such as artificial intelligence, augmented and virtual reality, and blockchain, the article elucidates how these innovations are revolutionizing the viewer experience. Additionally, exploring emerging trends, including metaverse integration and the increasing demand for live and interactive content, provides critical insights for industry practitioners. As the industry grapples with content overload and the intricacies of revenue models, this article propounds strategic considerations for navigating the evolving streaming landscape. Ultimately, this comprehensive examination illuminates the future trajectory of the streaming industry and underscores the imperative for innovative adaptation.

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Intelligent aerial video streaming: Achievements and challenges

Cited by 13 publications

References 107 publications

Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution

Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution

Peculiarities of video streaming industry development

Streaming ahead: technological innovations redefining new product development in the entertainment industry

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