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A Framework for Meta-Learning in Dynamic Adaptive Streaming over HTTP

Journal: International Journal of Computing, Communications and Networking (IJCCN) (Vol.12, No. 2)

Publication Date:

Authors : ;

Page : 3-11

Keywords : meta-learning; streaming; DASH; QoE;

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Abstract

This work presents a framework with a taxonomy on meta-learning used in Dynamic Adaptive Streaming over HTTP (DASH). With the increasing complexity of network conditions and user preferences, there is a need for efficient adaptation mechanisms in DASH to provide optimal quality of experience (QoE) for users. Meta-learning, or learning to learn, has emerged as a promising approach to enhance adaptive streaming algorithms in DASH by leveraging prior knowledge and experiences. The proposed framework provides a systematic and structured approach for applying meta-learning techniques in the context of DASH. It encompasses essential components, including data collection and preprocessing, meta-model architecture, meta-training, meta-testing, fine-tuning, and continuous improvement. The taxonomy within the framework categorizes various aspects of meta-learning in DASH, such as meta-learning approaches, components, objectives, and applications.

Last modified: 2023-06-29 18:53:28