Energy harvesting (EH) endows device-to-device (D2D) communication and cellular equipment with the ability of continuous communication to provide internet-of-things (IoT) services in natural areas. While the available energy, which relies on EH, becomes an extra nonnegligible factor in resource allocation. Besides, we integrate uplink non-orthogonal multiple access (NOMA) with D2D communication to provide multiple access for D2D transmitters for more efficient IoT service and more efficient utilization of limited spectrum. In this scenario, ingenious resource allocation approach is a key focus for utilizing the advantages in energy and spectral efficiency. Aiming to investigate the inherent resource allocation issue, we set our goal as maximizing the energy efficiency for both NOMA-based D2D groups and cellular users (CUs), where the power and spectrum allocation are both considered. Then we propose a two-stage game approach, which is theoretically proved to be capable of obtaining the equilibrium and a stable result, to solve the unilateral energy efficiency maximization problems. Besides, an energy-aware screening method is proposed to reduce the computations based on the available energy of user equipment. Finally, the effectiveness of our proposed method is verified through elaborated simulation results.

Integrated terrestrial-satellite networks (ITSNs) are the promising trends of future networks. However, there exist great challenges when performing video multicast in ITSNs due to the strong heterogeneity of users in comprehensive satellite coverage and the inter-system complex co-channel interference. To overcome these, we make the first attempt to propose an efficient cooperative robust video multicast (CRVM-ITSN) framework in ITSNs. The basic idea is to leverage the non-orthogonal multiple access technique in the cooperative transmission of ITSNs to achieve high-efficiency robust video multicast. The desired robust multicast performance realizes that the recovered video quality can adapt to diverse channel conditions of users. In CRVM-ITSN, to achieve the optimal cooperative transmission performance, power allocation and chunk scheduling of video data are jointly formulated as a distortion minimization problem, which is a non-convex mixed integer non-linear programming problem. To solve it, we design a provably convergent optimal algorithm by converting it to be convex. Besides, based on the theorem on optimal chunks selection of satellite cooperative transmission, a low-complexity chunk grouping algorithm is proposed to accelerate the optimal algorithm. Simulation results have demonstrated the superiority of proposed CRVM-ITSN against existing reference schemes, achieving about 4.1dB more gains in the recovered video quality.

To minimize the number of expensive global links, Dragonfly topology is developed greatly in today's data centers. However, deploying Remote Direct Memory Access (RDMA) applications inside Dragonfly requires the network to provide a routing scheme running at the flow level to avoid packet disorder. The existing Dragonfly routing scheme uses queue length to estimate link load, which is not a reasonable criterion for flow-level routing. In this paper, we use the amount of remaining data of flows to estimate the flow completion time and propose our routing scheme, named Remaining Data-based Adaptive Load-balance (RDAL). We compare the performance of RDAL with another routing scheme at the flow level. Our simulation shows that for flow-level routing, RDAL provides improvements in both average flow completion time and saturation throughput, especially in the adversarial traffic pattern. At most, RDAL can increase saturation throughput by 12% and reduce average flow completion time by 34% than UGAL, the state-of-the-art routing scheme for Dragonfly,

In recent years, with the explosive growth of visual sensors and a large number of related video applications in Internet of Things (IoT), massive video data is generated by IoT devices. Since the volume of video data is far greater than traditional data in IoT, it is challenging to ensure high Quality of Service (QoS) for video uplinking in IoT. To address this challenge, we integrate non-orthogonal multiple access (NOMA) and scalable video coding (SVC) in IoT. To improve the video quality, we formulate a power allocation problem to maximize the average QoS in the proposed integrated system. Due to that the problem is non-convex, we transform it into a monotonic problem based on its hidden monotonicity. Then a power allocation algorithm based on polyblock outer approximation is proposed to solve the problem effectively. Finally, simulation results demonstrate that the proposed algorithm outperforms existing OMA and NOMA based schemes for video uplinking in IoT in terms of QoS and energy efficiency.

To cope with rapid growth of video services, we propose a non-orthogonal multiple access (NOMA) based scalable video multicast (NOMA-SVM) framework for mobile networks, by exploiting NOMA's specific potential in scalable video multicast transmission. We consider statistical channels, instead of channels with perfect estimation, in the proposed NOMA-SVM framework in order to capture the realistic channel behaviors. As quality of experience (QoE) is a better metric than throughput for video transmission, QoE-driven power allocation is performed among multiple video layers in the proposed NOMA-SVM framework, in which users can decode video with quality proportional to their channel conditions. Specifically, we formulate the power allocation problem with the goal to maximize the average QoE over all users while guaranteeing the basic services of these users. To solve such a non-convex discrete problem, an optimal algorithm is developed based on the hidden monotonicity of the problem. A suboptimal algorithm is also proposed with much lower complexity in order to meet the practical needs. Simulation results show that the proposed algorithms outperform existing orthogonal multiple access (OMA) and NOMA based algorithms under various multicast scenarios in terms of QoE.

Limited backhaul bandwidth and blockage effects are two main factors limiting the practical deployment of millimeter wave (mmWave) networks. To tackle these issues, we study the feasibility of relaying as well as caching in mmWave networks. A user association and relaying (UAR) criterion dependent on both caching status and maximum biased received power is proposed by considering the spatial correlation caused by the coexistence of base stations (BSs) and relay nodes (RNs). Using stochastic geometry tools, we decouple the UAR and caching placement issues by analyzing the relationship between UAR probabilities and caching placement probabilities. We then optimize the formulated caching placement problem based on polyblock outer approximation by exploiting the monotonic property in the general case and utilizing convex optimization in the noise-limited case. Accordingly, we propose a BS and RN selection algorithm where caching status at BSs and maximum biased received power are jointly considered. Experimental results demonstrate a significant enhancement of backhaul offloading using the proposed algorithms, and show that deploying more RNs and increasing cache size in mmWave networks is a more cost-effective alternative than increasing BS density to achieve similar backhaul offloading performance.

In multi-cell non-orthogonal multiple access (NOMA) systems, designing an appropriate user grouping strategy is an open problem due to diverse quality of service (QoS) requirements and inter-cell interference. In this paper, we exploit both game theory and graph theory to study QoS-aware user grouping strategies, aiming at minimizing power consumption in downlink multi-cell NOMA systems. Under different QoS requirements, we derive the optimal successive interference cancellation (SIC) decoding order with inter-cell interference, which is different from existing SIC decoding order of increasing channel gains, and obtain the corresponding power allocation strategy. Based on this, the exact potential game model of the user grouping strategies adopted by multiple cells is formulated. We prove that, in this game, the problem for each player to find a grouping strategy can be converted into the problem of searching for specific negative loops in the graph composed of users. Bellman-Ford algorithm is expanded to find these negative loops. Furthermore, we design a greedy based suboptimal strategy to approach the optimal solution with polynomial time. Extensive simulations confirm the effectiveness of grouping users with consideration of QoS and inter-cell interference, and show that the proposed strategies can considerably reduce total power consumption comparing with reference strategies.

To cope with rapid growth of video traffic and energy consumption in mobile networks, we propose a spatial modulation (SM) and non-orthogonal multiple access (NOMA) integrated system for multi-user video transmission (SNIS-MUVT). SNIS-MUVT attempts to exploit the benefits from SM and NOMA to achieve high energy efficiency and high throughput. The main contribution of SNIS-MUVT lies in cross-layer design for video-aware transmission and quality of experience (QoE)-driven power allocation. With scalable video coding, base layers (BLs) are transmitted in the spatial domain while enhancement layers (ELs) are transmitted in the signal domain. In the spatial domain, layered SM is performed to support multi-user transmission according to the required BL rates and channel conditions of different users. In the signal domain, ELs are first extracted based on the estimated channel capacity. Then, the extracted ELs are handled by unequal error protection according to their importance before superposed for NOMA transmission. We formulate the QoE-driven power allocation problem in SNIS-MUVT. Both optimal algorithm based on the hidden monotonicity of the problem and suboptimal iterative algorithm with polynomial time are proposed. Finally, simulation results demonstrate that the proposed SNIS-MUVT outperforms existing SM and SM-NOMA integrated schemes in terms of QoE under various scenarios.

Recently, non-orthogonal multiple access (NOMA) has been proposed to improve spectral efficiency greatly over conventional orthogonal multiple access (OMA). However, it is still challenging to implement NOMA into transmission of video. In this research, we investigate video broadcasting design over NOMA systems. When video transmission is considered, quality of experience (QoE) is a better metric for NOMA design than throughput. Therefore, we design a QoE-driven scalable video broadcasting (SVB) scheme over NOMA systems. To achieve scalable transmission performance, power allocation is optimized among multiple video layers, which are requested by users with different channel conditions. Specifically, we formulate the power allocation problem to maxmize the system overall QoE while guaranteeing the basic service of users. Although this problem is non-convex and discontinuous, an optimal algorithm is proposed based on the hidden monotonicity of the problem. Simulation results show that the proposed SVB scheme over NOMA systems outperforms existing OMA and NOMA based schemes in various broadcast scenarios in terms of QoE.