MEC for Fair, Reliable and Efficient Media Streaming in Mobile Networks

Martín, Ángel; Viola, R.; Zorrilla, Mikel; Flórez, Jesús; Angueira, Pablo; Montalbán, Jon

doi:10.1109/tbc.2019.2954097

Cited by 17 publications

(5 citation statements)

References 46 publications

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“…Although the network structure of the neural network algorithm is simple and the efficiency of the protection and recognition process is fast, its training method relies too much on the database, which eventually leads to the low accuracy of the big data analysis system. Therefore, the big data analysis system established by the neural network algorithm is always not suitable for solving the problem of private data with popular nature [6,7]. For the self-media system, due to the increasingly large privacy data sets of different users and the increasing number of internal neuron nodes, the control and prediction of each neuron node become more and more difficult.…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

2022

Wireless Communications and Mobile Computing

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In recent years, with the in-depth research on the privacy protection methods of We-media network, various types of big data analysis technologies gradually protect the network privacy data, but there are still problems of low intelligence and poor protection in the existing research. Based on this, this paper first uses big data technology and artificial intelligence deep learning algorithm to complete the construction of different types of self-media control databases. Then, it analyzes the common privacy data types of We-media network, constructs an optimization protection model based on secondary identification and verification strategy, and forms a data query system. Finally, simulation experiments are conducted to verify whether the constructed network privacy protection model can realize the intelligent protection of network privacy algorithms from different dimensions. In the process of privacy protection of experimental data at different stages, the internal correlation differences of different types of protection algorithm strategies are obvious in the multidimensional analysis of specific databases. For different types of factor data in different types of We-media networks, the protection rate of the We-media network privacy protection model designed in this study has reached more than 95%. The research results show that the self-media network privacy protection model based on big data and artificial intelligence deep learning technology can realize protection from the aspects of gateway verification and data encryption and has high accuracy and reliability.

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Section: Introductionmentioning

confidence: 99%

Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

2022

Wireless Communications and Mobile Computing

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“…ETSI has initiated MEC standardization to promote and accelerate the advancement of edge-cloud computing in mobile networks, with two objectives: reduce traffic bottlenecks in the core and backhaul networks, and offload heavy computational tasks from power-constrained UE to the edge [31]. Consequently, MEC is used to enable low-latency or contextaware services [32], expanding and increasing the quality of the services [33].…”

Section: B Multi-access Edge Computingmentioning

confidence: 99%

Live Free-Viewpoint Video in Immersive Media Production Over 5G Networks

Pérez

Corregidor²,

Garrido

et al. 2022

IEEE Trans. on Broadcast.

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5G is a key tool for the cloud-based wireless production of audio-visual contents. By providing higher throughput and lower latency than previous mobile network technologies, as well as flexible allocation of network resources, it enables that some content production pipelines, which are currently implemented using wired connectivity, are provided on top of a 5G network. This brings down production costs, reduces environmental impact and increases operational efficiency. In particular, live immersive production services, such as free-viewpoint video (FVV), can be provided in a much more efficient way. In this paper, we address the challenge of producing and streaming FVV services in real time through 5G networks. We have adapted a state-of-the-art FVV system to integrate it within a 5G architecture, using three key technological enablers: mmWave radio access network to support the uplink traffic requirements from FVV cameras, multi-access edge computing to run video processing algorithms with minimum latency, and end-to-end slicing to guarantee a sufficient quality of service (QoS) within the production pipeline. We have built a field trial over the production network of a telecommunication operator, including a mmWave pilot deployment, edge cloud processing, and remote content production, involving three different locations across Spain. We have measured the key performance indicators at the relevant parts of our trial deployment, showing that, with existing 5G technology, it is possible to achieve live FVV production, although with some limitations. We have also analyzed such limitations, obtaining some insights on how the next generation of 5G networks can overcome them to achieve higher quality of experience (QoE).

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“…The CDNs are usually deployed in points of presence of Internet service providers [132], thus, they are located just outside the cellular network [133]. Recently, proposals have been made to deploy new network functions that reside closer to the users (e.g., co-located with base stations), and obtain radio link information to dynamically select CDNs accordingly [134]. Moreover, local caching at base stations can further reduce the stress on CDNs, for example, during live streaming events [134].…”

Section: Deploying Edge-c3mentioning

confidence: 99%