本文探討了針對 5G 網路和車聯網 (IoV) 的網路最佳化策略。研究內容包括在搭配移動邊緣計算 (MEC) 伺服器的 5G 網路中，透過快取管理來最佳化 MPEG-DASH 視訊串流的技術。此外，本文解決了在霧計算架構中網路地址轉換 (NAT) 的負載平衡問題，並提出了一種新穎方法，採用支持向量機 (SVM) 模型。而隨後更進一步整合了霧計算與雲計算，提出了一種軟體定義無線網路 (SDWN) 架構，以提升車聯網傳輸效率。除此之外，引入了基於 Q 學習的協作快取算法 (Q-LCCA) 用於蜂窩網路。結論中特別強調了這些方法在 5G 和物聯網 (IoT) 背景下，對於提升網路性能、減少延遲並改善用戶體驗的重要性。

This thesis explores network optimization strategies for 5G networks and the Internet of Vehicles (IoV). It delves into optimizing MPEG-DASH video streaming through cache management in 5G networks with Mobile Edge Computing (MEC) servers. It also addresses load balancing for Network Address Translation (NAT) traversal in fog computing architectures, proposing a novel approach using a Support Vector Machine (SVM) model. The thesis further explores enhancing IoV transmission efficiency by integrating fog computing and cloud computing, proposing a software-defined wireless network (SDWN) architecture. Additionally, it introduces a Q-learning-based collaborative caching algorithm (Q-LCCA) for cellular networks. The thesis concludes by highlighting the significance of these approaches in enhancing network performance, reducing latency, and improving user experience in the context of 5G and IoT.

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