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.

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.

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.

The dual connectivity is emerging as a promising solution to boost capacity in heterogeneous networks. However, it is challenging to obtain an optimal user association in heterogeneous networks with dual connectivity, due to its non-convex and combinatorial nature. In this paper, we propose a user association scheme based on deep reinforcement learning to maximize the overall network utility, which takes both throughput and user fairness into account, in the downlink of a heterogeneous network. Particularly, each user associates with the macro base station (BS) and a micro BS. We apply a deep Q-network (DQN) to obtain the nearly optimal policy to associate the users and micro BSs. Simulation results demonstrate that DQN-based user association performs better compared to the conventional user association schemes in heterogeneous networks with dual connectivity, and it behaves good scalability when the environment changes.

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 this paper, we investigate the fundamental tradeoff between energy efficiency (EE) and delay for time-varying and interference-free wireless networks. We formulate the problem as a stochastic optimization model, which optimizes the system EE subject to network stability and the average and peak transmit power constraints. By adopting the fractional programming theory and Lyapunov optimization technique, a general and effective algorithm, referred to as the EE-based dynamic power allocation algorithm (EE-DPAA), is proposed. The EE-DPAA does not require any prior knowledge of traffic arrival rates and channel statistics, yet yields an EE that can arbitrarily approach the theoretical optimum achieved by a system with complete knowledge of future events. Most importantly, we quantitatively derive the EE-delay tradeoff as$[O(1/V),O(V)]$with$V$as a control parameter for the first time. This result provides an important method for controlling the EE-delay performance on demand. Simulation results validate the theoretical analysis on the EE-delay tradeoff, as well as show the adaptiveness of the EE-DPAA.

Traditional load balancing schemes only considered two cells cooperation that is less likely to succeed. A novel scheme, load balancing by cells- cooperation-chains (C$^3$LB), is proposed. C$^3$LB establishes multi-level cells-cooperation-chains (C$^3$) to transfer traffic and extents the conditions of traffic transfer. We formulate a minimum-level C$^3$ selection problem and propose a simple algorithm to solve it. In addition, we present a C$^3$LB protocol to execute the found C$^3$. Numerical results show that as the maximum allowed levels of C$^3$ increase, the system call blocking probability decreases. Finally, we give the proposed value of the maximum allowed levels.

With the development of heterogeneous networks, concurrent transmission of Multimode-User Equipment (MUE) within multiple Radio Access Technologies (RATs) can improve transmission reliability and boost system performance. In this paper, some novel splitting strategies combining with different queuing management architectures are presented to obtain the multi-radio transmission diversity gain effectively. Two- dimensional discrete-state continuous-time Markov process is used to analyze our strategies and closed-form solutions have been made. Simulation results demonstrate that our proposed flow splitting strategies utilize the system resources efficiently and outperform current strategies.