As an emerging type of Internet of Things (IoT), Internet of Vehicles (IoV) denotes the vehicle network capable of supporting diverse types of intelligent services and has attracted great attention in the 5G era. In this study, we consider the multimedia content caching with multicast beamforming in IoV-based vehicular edge networks. First, we formulate a joint vehicle-to-vehicle- (V2V-) assisted clustering, caching, and multicasting optimization problem, to minimize the weighted sum of flow cost and power cost, subject to the quality-of-service (QoS) constraints for each multicast group. Then, with the two-timescale setup, the intractable and stochastic original problem is decoupled at separate timescales. More precisely, at the large timescale, we leverage the sample average approximation (SAA) technique to solve the joint V2V-assisted clustering and caching problem and then demonstrate the equivalence of optimal solutions between the original problem and its relaxed linear programming (LP) counterpart; and at the small timescale, we leverage the successive convex approximation (SCA) method to solve the nonconvex multicast beamforming problem, whereby a series of convex subproblems can be acquired, with the convergence also assured. Finally, simulations are conducted with different system parameters to show the effectiveness of the proposed algorithm, revealing that the network performance can benefit from not only the power saving from wireless multicast beamforming in vehicular networks but also the content caching among vehicles.

中文翻译：

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车辆边缘网络中的联合V2V辅助的群集，缓存和多播波束成形

车联网（IoV）作为一种新兴的物联网（IoT），是指能够支持多种类型的智能服务的车网络，并在5G时代引起了极大的关注。在这项研究中，我们考虑了基于IoV的车辆边缘网络中具有多播波束成形的多媒体内容缓存。首先，根据服务质量（QoS）约束，我们制定了车辆对车辆（V2V）联合辅助的群集，缓存和多播优化问题，以使流量成本和电力成本的加权总和最小化每个多播组。然后，通过两时标设置，将棘手且随机的原始问题在单独的时标上解耦。更确切地说，在较大的时间范围内，我们利用样本平均逼近（SAA）技术来解决联合V2V辅助的聚类和缓存问题，然后证明原始问题与其松弛线性规划（LP）对应项之间的最优解是等效的；在较小的时间尺度上，我们利用连续凸逼近法（SCA）解决了非凸多播波束成形问题，从而可以得到一系列凸子问题，同时也保证了收敛性。最后，在不同的系统参数下进行了仿真，以证明所提算法的有效性。结果表明，该网络性能不仅可以受益于车载网络中无线多播波束赋形节省的功率，而且还可以受益于车辆之间的内容缓存。