To enhance the adaptability and performance of multi-stream transmissions over non-ergodic block fading (BF) channels, this paper studies a multilevel-priority incremental-redundancy hybrid ARQ (MP-IR-HARQ) system with nested root-protograph-based low-density parity-check (NRP-LDPC) codes. Specifically, we analyze the performance of traditional RP-LDPC codes and reveal that they are unable to achieve full diversity in the IR-HARQ system over non-ergodic BF channels. To address this limitation, we propose a design methodology to construct a family of NRP-LDPC codes having the full-diversity property, throughput-limit-approaching property, outage-limit-approaching performance, and rate compatibility tailored for the IR-HARQ system. Additionally, we incorporate a multilevel-priority design composed of the adaptive modulation, adaptive detection, and successive decoding into the traditional IR-HARQ framework. The designed MP-IR-HARQ system can support the transmission of multiple data streams while providing unequal error protection (UEP) across them. The outage-boundary analyses, throughput analyses, and word-error-rate (WER) simulations demonstrate that the proposed NRP-LDPC-coded MP-IR-HARQ system can not only significantly outperform prior-art counterparts over non-ergodic BF channels, but also enable the multi-data-stream-based UEP transmission.

Device‐to‐device (D2D) communication provides the communication of the users in the vicinity and thereby decreases end‐to‐end delay and power consumption. More importantly, D2D communication enables offloading the traffic load of the base station (BS), and it is very suitable for caching, especially in ultra dense networks (UDNs). In this paper, a joint optimisation problem of collaborative caching and sleeping strategy based on energy‐delay tradeoff for D2D‐aided UDN is investigated. To solve the joint optimization problem, we decompose it into sleeping sub‐problem and collaborative caching sub‐problem. For sleeping subproblem, the optimal sleeping ratio is first derived, then, a delay‐aware sleeping strategy is proposed to obtain the local optimal sleeping scheme. For the collaborative caching subproblem, the suboptimal solution is obtained by distributed iterative method. In each iteration step, the suboptimal solution is derived by solving the combinatorial optimisation problem under Karush‐Kuhn‐Tucker conditions. Simulation results show that the proposed algorithms can coverage to the global solution. It also demonstrate that increasing caching capacity shortened mean delay, and benefitting from collaborative caching, delay performance and energy saving can be improved significantly. Moreover, it can be seen that combining the sleeping strategy with collaborative caching further reduced energy consumption by a considerable amount.