Comparative Study of One-Shot Learning in Dynamic Adaptive Streaming over HTTP : A Taxonomy-Based Analysis

Dynamic Adaptive Streaming over HTTP (DASH) has revolutionized multimedia content delivery, enabling efficient video streaming over the internet. One-shot learning, a machine learning paradigm that allows recognition of new classes or objects with minimal training examples, holds promise for enhancing DASH systems. In this comparative study, we present a taxonomy-based analysis of one-shot learning techniques in the context of DASH, examining four taxonomies to provide a comprehensive understanding of their applications, evaluation metrics, and datasets. The first taxonomy focuses on categorizing one-shot learning techniques, including siamese networks, metric learning approaches, prototype-based methods, and generative models. This taxonomy reveals the diversity of techniques employed to tackle one-shot learning challenges in DASH environments. The second taxonomy explores the applications of one-shot learning in DASH. It highlights areas such as video quality prediction, buffer management, content adaptation, and bandwidth estimation, shedding light on how one-shot learning can optimize streaming decisions based on limited or single examples. The third taxonomy addresses evaluation metrics for one-shot learning in DASH. It encompasses accuracy-based metrics, generalization metrics, latency-related metrics, and robustness metrics, providing insights into the performance and effectiveness of one-shot learning approaches under various evaluation criteria. The fourth taxonomy delves into dataset characteristics for one-shot learning in DASH. It categorizes datasets into synthetic datasets, real-world datasets, transfer learning datasets, and unconstrained datasets, enabling researchers to select appropriate data sources and evaluate one-shot learning techniques in diverse streaming scenarios. By conducting this taxonomy-based analysis, our study provides researchers and practitioners with a structured framework for understanding and comparing different aspects of one-shot learning in DASH. It highlights the strengths, weaknesses, and potential applications of various techniques, offers guidance on evaluation metrics, and showcases dataset characteristics for benchmarking and future research. Ultimately, this comparative study aims to foster progress in one-shot learning for DASH by facilitating knowledge exchange, inspiring new research directions, and promoting the development of efficient and adaptive multimedia streaming systems over HTTP.