USER-CENTRIC ADAPTIVE MULTIMEDIA STREAMING IN INTERACTIVE COMMUNICATION NETWORKS USING SHANNON-FANO GENETIC ALGORITHM
Abstract
In today’s rapidly evolving digital landscape, interactive communication networks play a pivotal role in facilitating real-time interactions among users. One of the critical challenges in these networks is ensuring the seamless delivery of multimedia content that caters to the diverse needs and preferences of individual users. This research endeavors to address this challenge by introducing a novel approach, where it places user satisfaction at its core, leveraging adaptive streaming techniques to dynamically adjust multimedia content delivery. By considering parameters such as network conditions, device capabilities, and user preferences, it optimizes the streaming experience in real-time. A key innovation lies in the integration of Shannon-Fano coding principles and genetic algorithms. Shannon-Fano coding enhances data compression efficiency, reducing bandwidth consumption, while genetic algorithms fine-tune the adaptive streaming parameters for each user. Our experimentation and evaluations demonstrate the effectiveness of this approach, showcasing improved multimedia streaming quality, reduced latency, and efficient bandwidth utilization. The synergy of user-centricity, adaptive streaming, Shannon-Fano coding, and genetic algorithms presents a promising avenue for enhancing multimedia communication in interactive networks.

Authors
Logeshwari Dhavamani1, A. Kaliappan2, M. Sakthivel3, V. Balaji4
St. Joseph’s College of Engineering, India1, P.A. College of Engineering and Technology, India2, Malla Reddy College of Engineering, India3, Mai Nefhi College of Engineering and Technology, Eriteria4

Keywords
User-Centric, Adaptive Multimedia Streaming, Interactive Communication Networks, Shannon-Fano Coding, Genetic Algorithm
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032031110011
Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 14 , Issue: 3 , Pages: 2965 - 2973 )
Date of Publication :
September 2023
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794
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33

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