The rise of mixed reality (MR) as a key mobile application draws interest. Wireless headsets in femtocell networks complicate resource optimization. This study proposes a resource allocation for volumetric MR streaming in multi-BS settings. Neural networks predict viewing directions; based on predictions, confidence, and layers, the algorithm customizes vision scope and quality to boost utility. Simulations show superior performance.

The emergence of mixed reality (MR) as a significant application in mobile networks has garnered significant attention. Wireless headsets enable unrestricted user movement within femtocell networks comprising numerous small base stations, offering a promising solution for MR applications. However, the complexity of these systems poses challenges in optimizing resource allocation across base stations. This paper proposes a novel resource allocation method for volumetric MR streaming in multi-base-station environments. The method consists of two phases. Firstly, the method uses neural networks to model and forecast users’ viewing directions. Leveraging these predictions, their confidence levels, and layer characteristics, the algorithm adjusts video quality for each user and allocates transmission resources across base stations to optimize overall performance. Through comprehensive analysis, we prove that this novel problem is NP-hard and show that our approach achieves a performance within a bounded gap from the optimal solution. Simulation results reveal that our proposed algorithm outperforms existing techniques, enhancing aggregate performance across diverse scenarios.

Mobile video streaming is an intriguing application for next-generation networks. Wearing the goggles that render two-eye videos, users can enjoy the interactive multimedia experience. Providing high-quality video streams to multiple mobile devices in specific areas will become popular in future cinemas, theme parks, and museums. To ensure quality wireless coverage for a good streaming experience, the next-generation wireless technology (e.g., 5G/6G) employing massive MIMO schemes is a promising solution. Massive MIMO transmissions can improve bandwidth utilization while maintaining acceptable system complexity through numerous transceiving antennae. To incorporate massive MIMO transmissions with mobile video streaming, an innovative cross-layer scheme is needed to flexibly and efficiently manage the antenna array for serving multiple user devices. This allocation mechanism must have low computational complexity and operate stably to prevent demand fluctuations from affecting the quality of service experienced by other users. In this work, we introduce a new problem of provisioning layer-encoded streams to mobile devices (e.g., VR goggles) by allocating antennae in the base stations’ massive MIMO arrays. Given each user’s bitrate demand, the available antennae of each femtocell, and the channel characteristics, the system allocates transmitting antennae to maximize the total system utility. Our theoretical analysis shows that this allocation problem is NP-hard but our proposed scheme provides bounded performance with polynomial-time complexity. We also discuss and justify the stability of our proposed new allocation mechanism. Simulations demonstrate that our scheme outperforms simple heuristic methods. To the best of our knowledge, this is the first attempt to tackle antenna allocation for mobile user devices in immersive video streaming using massive MIMO schemes.

Due to the growing need for bandwidth starving Multi-View Videos (MVV) in virtual reality, TV, and education, effectively allocating the resources of next-generation wireless technologies for MVV streams becomes increasingly crucial. To achieve high utility for MVV users, this article proposes a cross-layer resource allocation mechanism to leverage video synthesizing schemes (such as Depth-Image-Based Rendering (DIBR) for efficient MVV streaming with massive MIMO). First, we formulate a new problem,antenna allocation with video synthesis(AAVS), and prove its NP-hardness. Then, we design an approximation algorithm namedUtility-based Multi-View Synthesis(UMVS) with the analytical performance provided, and dynamic scenarios are addressed by augmenting UMVS with deep reinforcement learning. Data-driven simulation results show that UMVS outperforms existing antenna allocation schemes by at least 10%, and the DRL extension provides an additional 6% improvement in system utility under congested scenarios.

Due to the growing need for Multi-View Videos (MVV) in advertisement, TV, and education, effectively allocating the resources of next-generation wireless technologies to provide MVV streams is challenging. To achieve the high utility for MVV users, this paper proposes a cross-layer resource allocation mechanism to leverage video synthesizing schemes (such as Depth-Image-Based Rendering (DIBR)) for efficient MVV streaming with massive MIMO. We formulate a new problem Antenna Allocation with Video Synthesizing (AAVS) and prove its NP-hardness. Then, we design an algorithm, named Marginal Utility-Based Iteration (MUBI). The performance is evaluated by the data-driven simulations, and the results manifest that MUBI outperforms the baselines regarding the total utility of users.

Massive MIMO is an important research topic for incoming 5G communications. Such a technology uses numerous simultaneously transceiving antennas to better utilize radio resources and improve communication quality. Though massive MIMO is suitable for inter-basestation wireless backhaul networks, how to adequately adjusting each BS's antennas remains an unsolved issue. In this paper, we propose a distributed mechanism called Hierarchical Distributed Adjustment (HDA). Basing on links' channel conditions, base-stations' available antennas, users' service requirements and neighbors' allocation decisions, the transceiving antennas of each link is adjusted so as to both fulfill the QoS constraints and maximize the total utility of the best-effort traffics. The simulation results show a promising performance and adaptive behavior under various circumstances. To the best of our knowledge, this paper is the only work that focuses on this new issue and achieves such an effective and flexible allocation.

In this study, the authors tackle the problem of carrier aggregation (CA) in downlink of long‐term evolution advanced (LTE‐A) femtocell networks. They propose a novel approach in a new perspective: namely, user navigation, to improve the CA performance of an LTE‐A system in the indoor environment. The proposed indoor user navigation (IUN) algorithm exploits the a priori knowledge of radio interferences between femtocells to build a geometric quality‐of‐service (QoS) map, which can be utilised to navigate users toward the locations suitable for performing CA to satisfy the QoS requirements. The simulations demonstrate the effectiveness of the proposed IUN algorithm to improve the CA performance in terms of the aggregate throughput for the LTE‐A femtocell networks.

Content delivery networks (CDNs) have been widely implemented to provide scalable cloud services. Such networks support resource pooling by allowing virtual machines or physical servers to be dynamically activated and deactivated according to current user demand. This paper examines online video replication and placement problems in CDNs. An effective video provisioning scheme must simultaneously (i) utilize system resources to reduce total energy consumption and (ii) limit replication overhead. We propose a scheme called adaptive data placement (ADP) that can dynamically place and reorganize video replicas among cache servers on subscribers' arrival and departure. Both the analyses and simulation results show that ADP can reduce the number of activated cache servers with limited replication overhead. In addition, ADP's performance is approximate to the optimal solution.

Recently, Wireless Sensor Networks (WSNs) are spread all over the world, and are commonly used to collect physical information from the surrounding world. WSNs play a central role in the Internet of Things (IoT) vision. IoT is a new paradigm that aims to integrate and connect anything at anytime, anyplace, with anything and by anyone. However, addressing objects is the main challenge for IoT. IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) has been introduced to cope with this issue. The emerging paradigm Social web of thing (SWoT) enables users to manage, access, share, and integrate smart objects with Social Network Site (SNS). This paper investigates different SWoT platforms and architectures, provides an overview of the current state-of-the-art on WSN communication standards.

Received power in cellular networks is commonly employed in mobile localization systems. However, uncertainties in power-distance mapping and dynamics of propagation models challenge the performance of the positioning system. Although collecting realistic data in the target area may reduce the uncertainties, it requires a time-consuming site survey and high-cost labor efforts. This study proposes a novel algorithm to enhance the performance of mobile localization without the need of additional calibration effort. The proposed algorithm utilizes the pairwise information between base stations (BSs), which is assumed to be available, and then localizes the user based on multidimensional scaling. Unlike traditional methods, the proposed approach further considers the geometric structure between BSs to compensate for the problem of distance estimation, thus achieving more accurate location estimations. We applied this approach in a realistic GSM network and experimental results demonstrate the effectiveness of our approach. The proposed algorithm outperforms previous calibration-free positioning methods, including Cell-ID and enhanced Cell-ID, in reducing the mean error by 16.74%-38.56% and 18.22%-20.96%, respectively.

In recent years, multicast over wireless access networks has become a popular research topic and application. However, if relay nodes are supported, forming an efficient multicast topology is a challenging issue. In this paper, we propose a multi- hop multicast recipient maximization scheme for wireless relay networks. First, we formulate an important problem called Multi-Hop Recipient Maximization (MHRM), which involves allocating resources to the base-station and relay stations efficiently in order to maximize the number of recipients with the given budget and channel conditions. To solve MHRM, we propose a heuristic called Multicast Path Selection (MPS). We prove that MHRM is NP-hard, and also analyze MPS's computational complexity. The results of simulations conducted to evaluate the heuristic's performance demonstrate that, under variant conditions, MPS utilizes bandwidth resources and relay nodes effectively such that it significantly outperforms all existing approaches. To the best of our knowledge, MPS is the only scheme that focuses on this important issue and achieves such a satisfactory performance.

In this paper, we study the utility-based resource allocation problem for layer-encoded IPTV multicast service over WiMAX networks. In this problem, each video stream is encoded into multiple layers. We regard each layer as a multicast subsession. Each layer of a video stream is assigned a utility value, and the number of layers for each program each user can receive is adjustable. The objective is to maximize the total utility (i.e., all users' satisfaction) and the system resource utilization, subject to users' channel conditions, the popularity of a video program, and the total available radio resource. We design a polynomial-time solution to this problem, and show that the difference in the performance of our proposed mechanism and the optimal solution is tightly bounded. Our mechanism supports both unicast and multicast, and both single layer and multi-layer environments. Most importantly, it can be integrated with the multicast mechanism defined in WiMAX standards, and can also be applied to any kind of wireless networks which support adaptive modulation and coding schemes. The performance of our scheme is evaluated by simulation. The simulation results show that this scheme can allocate resource flexibly according to the utility function of each program, the popularity of each program, and the amount of total resource available in the network.

This paper proposes a dynamic fingerprinting combination (DFC) algorithm that improves mobile localization by dynamically weighting the spatial correlation from multiple location fingerprinting systems. This DFC algorithm first extracts the complementary advantages of fingerprinting functions to construct a fusion profile, and then dynamically combines individual outputs based on the fusion profile surrounding the test sample. The proposed algorithm generates more accurate location estimates and reduces the risk of selecting a poorly-performing fingerprinting approach. This study applies DFC to an actual GSM network with realistic measurements. Experimental results show that DFC improves the positioning accuracy of base fingerprinting algorithms, including the Bayesian approach and a neural network model.

In this paper, we propose an economics-based, distributed multicast scheme for wireless relay networks. Our goal is to construct efficient multicast routes that serve as many users as possible with the given bandwidth budget. The proposed scheme, called Price-Based Distributed Allocation (PBDA), allows each node to make decisions independently. Through analysis, we prove that the original problem is NP-complete, and show that our heuristic has polynomial-time complexity. We also conduct simulations to evaluate the scheme's performance. The results show that by utilizing relay nodes, PBDA can provide efficient and stable allocation of wireless resources. PBDA produces low-signaling overhead and can be deployed in a very efficient, high-performance manner.

In this paper, we tackle an important problem in WiMAX relay networks called Multicast Recipient Maximization (MRM), which maximizes the number of multicast recipients with given budget. To deal with this problem, a novel resource allocation scheme called Dynamic Station Selection (DSS) is proposed. We prove that MRM is NP-hard, while our DSS has polynomial-time computational complexity. The simulation results show that under different situations, the performance of DSS always approximates the optimal solution. With this scheme, multimedia streams can be efficiently multicast over IEEE 802.16j WiMAX Relay Networks.

In this paper, we consider the resource allocation problem for multicast over IEEE 802.16j WiMAX networks, which use relay stations to transmit data between the base station and subscriber stations to improve the transmission quality. Our objective is to maximize the total number of recipients by allocating resource among the base station and relay stations subject to the resource budget and the channel quality. We prove that the problem is NP-hard, and design a polynomial-time algorithm to solve it. The algorithm can be integrated with the multicast mechanism defined in the WiMAX standards, and can also be applied to any kind of wireless networks that support adaptive modulation and coding schemes. The performance of the algorithm is evaluated through simulations. We show that the typical routing approach is inefficient, whereas our scheme can always utilize relay stations to achieve a superior performance under different channel qualities and resource budgets.

In this paper, we study utility-based resource allocation for soft QoS traffic in infrastructure-based wireless networks. Soft QoS traffic here refers to the traffic which demands certain amount of bandwidth for normal operation but allows some flexibility when the given bandwidth is close to the preferred value. The resource requirement of soft QoS traffic can be described with sigmoid utility function. Our objective is to maximize the total utility of all soft QoS flows without going through a wireless bidding process. We develop essential theorems as the design guidelines for this problem, and then propose a sub-optimal, polynomial time solution based on the developed theorems. We prove that the difference in the performance of our mechanism and the optimal solution is bounded. The performance of the proposed solution is evaluated via simulations. The results show that our solution can adapt to any types of soft QoS flows. Specifically, it acts like a hard QoS system and allocates resource in a fairness-oriented manner when the utility functions of flows are unit-step functions; on the other hand, when the utility functions are concave, it behaves like a best effort system and allocates resource in a throughput-oriented way.

In this paper, we propose a utility-based resource allocation scheme for layer-encoded IPTV multicast streaming service over IEEE 802.16 WiMAX networks. Unlike existing utility-based schemes, this mechanism is designed for wireless networks which support adaptive modulation and coding. Each video stream (or program) is encoded into different layers. Then, our mechanism adjusts the number of each user's received layers dynamically according to its channel condition and the available network bandwidth, so as to maximize total utility. We prove that this problem is NP-hard, and show that our scheme is bounded in performance to the optimal solution and can run in polynomial time. The simulation results show that this scheme can allocate resource flexibly according to the utility function of each program, the popularity of each program (i.e., the number of users receiving each program), and the amount of total available resource in the network. The result also shows that the fairness of the system can be guaranteed.

In this paper, we study utility-based maximization for resource allocation in the downlink direction of centralized wireless networks. We consider two types of traffic, i.e., best effort and hard QoS, and develop some essential theorems for optimal wireless resource allocation. We then propose three allocation schemes. The performance of the proposed schemes is evaluated via simulations. The results show that optimal wireless resource allocation is dependent on traffic types, total available resource, and channel quality, rather than solely dependent on the channel quality or traffic types as assumed in most existing work.

In this paper, we study utility-based resource allocation for soft QoS traffic in wireless networks. We describe the design guidelines for this problem and propose a sub-optimal, polynomial time solution for such allocation. The proposed mechanism accounts for the traffic type, the total available resource, and the user channel quality, rather than solely depending on channel quality or traffic type as assumed in most existing work. The performance of the proposed mechanism is evaluated via simulations. The results show that this mechanism adapts to different traffic types. Specifically, it acts like a hard QoS system and allocates resource in a fairness-oriented manner when the utility function is very steep; on the other hand, when the utility function is relatively flat, this mechanism behaves like a best effort system and allocates resource in a throughput-oriented way.

In this paper, we study utility-based optimal resource allocation in wireless networks. Utility here refers to a function which describes the degree of user satisfaction with a certain amount of allocated resource. We introduce utility function maximization in a centralized wireless environment and consider two types of traffic, i.e., best effort and QoS, the characteristics of which can be described by two existing utility functions. An allocation for the mixture of QoS and best effort traffics is proposed and its performance is evaluated via simulations. From this study, we show that optimal wireless resource allocation depends on the traffic types, the total available resource, and the channel quality, rather than solely dependent on the channel quality or traffic types as assumed in most existing work.

In this paper, we study the code assignment problem in high speed downlink packet access (HSDPA) networks. In HSDPA, the spreading factor of codes is fixed at 16, and the maximum number of bits reserved for code assignment transmitted on the control channel is limited to 64 bits. This high control overhead may lead to inefficiencies in HSDPA systems when the number of mobile nodes in a cell becomes large. Existing code assignment mechanisms allow codes with different spreading factors to be allocated, rendering them inadequate for HSDPA systems. In this paper, a new segment-based code assignment mechanism is proposed. In our mechanism, 16 codes are grouped into several segments. Since the number of codes in each segment is reduced, the corresponding signaling overhead is also reduced. The load of each channel is then balanced in each segment so as to maximize the system performance.