Collaborative Video Caching Strategy Based on Delay and Energy for Software-Defined Hybrid VLC and mmWave Networks

The integration of 5G networks has revolutionized wireless communication with higher data rates, and ultra-low delay, enabling new services and applications, especially in mobile edge computing (MEC). MEC aims to minimize delay and enhance user experience by placing computational power and storage capabilities closer to end-users. A key challenge in MEC is efficiently managing network resources, particularly in environments with hybrid Radio Access Technologies (RATs). This paper explores a collaborative video caching strategy focused on delay and energy efficiency for software-defined hybrid Visible Light Communication (VLC) and millimeter Wave (mmWave) networks. These technologies offer high data rates and low interference but face deployment challenges like varying channel conditions and susceptibility to blockages. The paper proposes a novel framework leveraging Software-Defined Networking (SDN) for dynamic resource management and optimized content caching in hybrid VLC and mmWave networks to address these issues. It integrates a Long Short-Term Memory (LSTM) network to forecast user preferences and a collaborative communication framework for content sharing among nodes, enhancing edge caching benefits. The study formulates optimization problems for minimizing content access delay and energy consumption, considering the limited storage capacities at small-cell base stations (SBSs), and develops a collaborative edge caching algorithm for efficient caching placement. Numerical results show significant improvements in delay reduction, energy consumption, and cache hit rates. DOI: 10.61416/ceai.v27i1.9186