Recently, scalable video coding (SVC) and scalable semantic coding (SSC) have emerged as effective solutions for supporting online video streaming in applications such as remote cockpits and telemedicine. Although the significance of SVC and SSC is well established, the underlying principles related to their performance bounds have not been thoroughly investigated. To address this issue, we study the performance bounds for online SVC/SSC in this paper. Initially, to evaluate coding performance, we propose a new metric referred to as effective coding gain (ECG), which jointly considers the entropy of the source data and the mutual information between the source and the encoded video data. Next, from the perspective of information theory, we derive a closed-form expression for the ECG bound while accounting for the impacts of diverse SVC/SSC coding structures. Our results not only ensure that the performance bounds can be efficiently evaluated but also provide valuable insights for resource allocation, cost optimization, performance evaluation and comparison, as well as for understanding the optimization space of existing systems.

For unmanned aerial vehicles (UAVs), remote control relies heavily on real-time video streaming. Given the limitations of hardware resources and the variability of channel conditions in mobile UAV communications, scalable video coding (SVC) has become increasingly important, as it allows for bitrate adaptation while maintaining low coding complexity for UAVs. Although SVC is essential for UAVs, the mathematical relationship between video bitrate and coding complexity has not been thoroughly investigated. To address this challenge, this paper proposes using information theory to study the performance upper bounds of UAV-SVC concerning the control of computational complexity. Specifically, we introduce a method that formulates coding performance using mutual information-based closed-form expressions. Furthermore, based on the proposed method, we explore the fundamental principles that enable us to determine the performance bounds of a UAV-SVC scheme with very low computational complexity. Consequently, the findings of this paper offer a comprehensive understanding of SVC for UAV video streaming.

Low Earth Orbit (LEO) satellite network can provide the seamlessly global coverage and relatively low propagation delay, which has been envisioned as a key component of future 6G communication. However, the highly dynamic network topology and quite limited communication and computation resources of LEO satellites, alongside with uneven traffic distribution and demand of ground users impose a severe challenge for the routing algorithm design. To tackle this issue, we propose a ground block division and topology switching routing algorithm with software-hardware separation to handle the frequent uploading network topology and routing tables issue and reduce the computational and communication overheads. Furthermore, we estimate the long-term traffic demand in different ground blocks, and calculate the potential traffic demand for each block, and devise the Traffic Distribution Oriented Load Balancing Routing Algorithm (TDOLBRA) to avoid bottleneck links and reduce unnecessary overheads. Moreover, considering different Quality of Service (QoS) requirements of ground users’ traffic, we classify the traffic into high priority traffic and regular traffic, and adopt different routing strategies to further promote the network performance and resources utilization. Simulations results on STK and OPNET demonstrate that the proposed TDOLBRA scheme can have superior performance over three existing methods. To be specific, our proposed TDOLBRA scheme can achieve the best performance with respect to the fairness index, average traffic per link and packet drop rate Ploss. Especially, compared with three benchmark schemes, Ploss under our scheme is reduced by 3.4x, 7.6x, and 13.2x respectively when the total traffic is 18000 packets/min.

Scalable video coding (SVC) and power division multiplexing (PDM) are crucial technologies for video streaming in internet-of-vehicle (IoV) networks due to their robustness over time-varying channels. However, the correlation of video tiers and channel fluctuations induce significant complexity in the joint design of SVC and PDM in IoV networks. To reduce such complexity, we propose a novel transmission strategy for SVC-based video streaming in PDM technology by facilitating cross-layer control across the application, data link, and physical layers. Specifically, we propose a concise framework where the modulation order, the power control, and prioritization of video tiers are jointly controlled. Moreover, we leverage the network abstraction layer units within an arbitrary group-of-pictures to improve the overall video data rate. The formulated joint control of the modulation order, the transmission power, and video-tier prioritization is proved to be a quasi-concave problem. Consequently, our proposed cross-layer optimization based SVC video transmission strategy could efficiently utilize the prioritized video tiers in the application layer, power control in the data-link layer, and modulation orders in the physical layer. Numerical results are used to demonstrate performance improvements over the benchmarks.

Recently, scalable video coding (SVC) has gained significant recognition in mobile video streaming because it can adapt bitstreams to time-varying transmission conditions. However, the coding performance of SVC, which is determined by its coding structure, has not been thoroughly studied. To address this issue, we propose analyzing the redundancy, reduction, distortion, and mutuality of video information within the video coding processes. This analysis facilitates the development of a novel information-theoretical framework for quantifying coding performance, which includes an information theory (IT)-based quantification method and a graphical representation system. The representation system accurately delineates the coding reference structure for encoding each video frame, while the proposed method utilizes mutual information to quantify the achievable coding performance of SVC under the delineated structure. To demonstrate the significance of our research, we apply the proposed framework to encode a basic coding unit, showcasing its effectiveness in improving SVC schemes. Consequently, our framework not only provides an efficient approach for quantifying the coding performance of SVC but also serves as an invaluable tool for optimizing SVC in various applications.

The massive-antenna is widely considered as one of key technologies in the 5th generation (5G) networks and beyond. To approach the system capacity with the massive-antenna, it is highly demanded for fundamental research on how to obtain the system capacity of massive-antenna based broadcast channel (BC) with simple closed-form expressions. To address this, our paper focuses on the massive-multiple-input single-output (mMISO) BC transmission, which is a common scenario in cellular networks. With regarding the massive-antenna, a precise approximation of the system capacity is derived with a simple closed-form expression. The simulations validate the correctness of the derived outcomes in mMISO-BC.

For the physical (PHY) layer in the 5th generation (5G) networks, there may be only time division multiplexing (TDM) and orthogonal frequency division multiplexing (OFDM) as candidates to serve multiple information flows via a single wireless link. Such two multiplexing schemes require the timing/ frequency synchronizations strictly, which is not suitable for power division based medium access control (MAC) protocols, i.e., non-orthogonal multiple access (NOMA). To address this, associating with multiple-input multiple-output (MIMO) techniques, a power division multiplexing (PDM) scheme is proposed as an alternative option to MIMO-TDM/MIMO-OFDM. The proposed MIMO-PDM directly utilizes the division of transmit power to replace the conventional time-slot/sub-band for the different information flows. Thus, it owns high compatibility with NOMA which is a promising multiple access protocol in 5G. With regarding the quality of service (QoS) required by the information flows, this paper derives the optimum power division of MIMO-PDM in conditions of multiple-input single-output (MISO), single-input multiple-output (SIMO), and MIMO, respectively. Additionally, we study the optimum pre- and post-coding of MIMO when serving arbitrary number of information flows. The proposed optimization algorithms of MIMO-PDM own reasonable computational complexity which is not higher than the classic water-filling algorithms. Consequently, the proposed MIMO-PDM could efficiently achieve the optimum performance as well as ensure the QoS of multiple information flows.

As the mobile communication technologies evolving, spectrum resource has become extremely scarce. Accordingly, spectrum aggregation (SA) is proposed as an emerging solution to efficiently utilize the dispersive resource. To address such a challenging issue, this paper introduces power domain into the conventional SA which only works in the time and frequency domains, and extends the resource block (RB), which is a time-frequency spectrum management unit in LTE standard, to a time-frequency-power spectrum unit termed resource cube. Based on this, a novel radio resource management (RRM) scheme is proposed to manage the spectrum with multiplexing division in time, frequency, and power domains (3D) simultaneously. Through theoretical derivations, we show that under certain simplifications, the proposed 3D RRM could be formulated as a convex objective function with linear constrains, and can be solved with low computational complexity. When compared with the conventional RB-based RRM, the proposed 3D RRM matches the real-time requirement in SA. Furthermore, it is proved that the proposed scheme could achieve better energy efficiency than the RB-based ones.

In cellular networks, since Media Access Control (MAC) layer plays a key role in every access equipment, it fascinates that little progress on multiple access protocol could save considerable energy. Accordingly, this paper studies a novel MAC protocol, i.e., the power division multiple access (PDMA) protocol, with the purpose of green communication. As a fundamental study of PDMA, we first propose a power division multiplexing (PDM) scheme, analogous to the time division multiplexing and frequency division multiplexing. It is proved that the transmit power could be divided into multiple regular power segments (PSs) to simultaneously transmit multiple independent information/data streams in peer to peer communications. Based on our fundamental studies of PDM, an orthogonal PDMA (OPDMA) protocol is proposed to utilize multiplexing and degraded channel gains for energy saving. By adopting the orthogonal PSs proposed in OPDMA, multiple information streams in different channels could be transmitted efficiently and concurrently with quality of service guarantee. This paper shows that the proposed OPDMA not only has low computational complexity as the conventional Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) protocols but also gains better energy efficiency, which consists with the energy saving requirement in green communications.

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users. A large number of applications demand for primary user behavior correlation in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship of primary users by introducing a Bayesian network (BN) based framework. How to learn such a BN structure is a long standing issue, not fully understood even in the statistical learning community. To solve such an issue in CR, this paper proposes a BN structure learning scheme which incurs significantly lower computational complexity compared with previous ones. Thus, with this scheme, cognitive users could efficiently understand the statistical pattern of primary networks.

In wireless communication, the amplitude and phase of the transmitted signal have certain patterns during one symbol duration, which introduces high correlation among the samples obtained by oversampling at the receiver. In this work, we aim to explore such correlation information for cognitive radios to enhance the performance of spectrum sensing. By jointly considering the signal modulation, multipath fading, and oversampling rate, we derive the distribution of the empirical autocorrelation function for the obtained samples, on which we propose two efficient spectrum-sensing algorithms, and then analyze their performance. Our theoretical results reveal that the proposed algorithms with oversampling perform strictly better than the conventional energy detection scheme, while requiring the same level of prior information. Finally, we show through simulations that the derived statistical characteristics approximate the true statistical distribution of the autocorrelation function well, and the proposed sensing algorithms significantly improve the sensing performance compared to several existing sensing schemes.

The architecture of decentralization makes future wireless networks more flexible and scalable. However, due to the lack of the central authority (e.g., BS or AP), the limitation of spectrum resource, and the coupling among different users, designing efficient resource allocation strategies for decentralized networks faces a great challenge. In this paper, we address the distributed channel selection and power control problem for a decentralized network consisting of multiple users, i.e., transmit-receiver pairs. Particularly, we first take the users' interactions into account and formulate the distributed resource allocation problem as a non-cooperative transmission control game (NTCG). Then, a utility-based transmission control algorithm (UTC) is developed based on the formulated game. Our proposed algorithm is completely distributed as there is no information exchange among different users and hence, is especially appropriate for this decentralized network. Furthermore, we prove that the global optimal solution can be asymptotically obtained with the devised algorithm, and more importantly, in contrast to existing utility-based algorithms, our method does not require that the converging point is one Nash equilibrium (NE) of the formulated game. In this light, our algorithm can be adopted to achieve efficient resource allocation in more general use cases.

In order to manage the whole resource of B3G system efficiently, radio resource mobility management is investigated in this paper. The whole management procedure consists of initial radio access technology selection and load balancing. The corresponding selection rules and ordering rules are designed. In addition, radio resource mobility management framework and the detailed implementations are also presented. The scheme we proposed benefits both user and network with lower dropping probability and higher immediately served probability than other existing and typical schemes. Furthermore, it is easily implemented and its computational complexity is low.

For cognitive radio (CR) networks with user hierarchy, the sensing strategy with "listen-before-talk" (LBT) policy plays a key role in spectrum utilization and primary user (PU) protection. However, existing sensing strategies do not handle satisfactorily the randomness of both user locations and channel conditions in the network environment, resulting in inefficient spectrum utilization. To cope with such randomness, this paper develops three dynamic sensing strategies that can adaptively schedule the sensing slots/cycles according to the online link conditions without assuming knowledge of the PU traffic model. The proposed strategies can improve the efficiency of spectrum utilization while being robust with respect to the uncertainty in the PU traffic pattern. To maximize spectrum utilization, the proposed strategies are formulated through closed-form expressions. Efficient methods are introduced to compute the optimal values of the parameters used in the strategies, such as sensing time and sensing threshold. Simulations verify that the proposed sensing strategies offer an evident improvement on the spectrum utilization of the CR network.

This letter studies cooperative spectrum sensing (CSS) in which secondary users efficiently cooperate to achieve superior detection accuracy with minimum cooperation overhead. We consider each cooperative user only spends "1 bit" on reporting its own sensing decision to data fusion center as the total cooperation overhead. However, this "1 bit" CSS could not gain better sensing outcomes in current data fusion rules (DFRs). To ameliorate this issue, we derive theorems to reveal the optimum threshold of general DFR. Then we propose three novel DFRs and related three algorithms to efficiently obtain the optimum decision threshold for different objectives. By simulations, the proposed DFRs indicate evident improvement on CSS performance.

In cognitive radio system, secondary users should have capability of detecting the signal from primary user to evade interfering harmfully with any primary receiver. The duration used for spectrum sensing influences the performance of secondary user system. In this paper, we show disadvantages of one threshold judgment in cognitive radio spectrum sensing. For improving this defect, we propose a double thresholds scheme and prove this scheme indeed could gain more throughputs under certain condition. Additionally, two instantiations are taken for general illustrating that our scheme performs better than traditional judgment at the conditions shown in these two examples. It is proved this condition is satisfied universally in cognitive radio system. Results indicate the maximal throughput increases while adopting our judgment scheme when parameters meet the condition.