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Fulcrum coding combines a high-field outer Random Linear Network Coding (RLNC) that generates outer coding expansion packets with a small-field inner RLNC that combines the source packets and the outer coding expansion packets. This two-layer Fulcrum coding allows flexible decoding in receivers with heterogeneous computational capabilities. Fulcrum coding has so far only been studied for conventional dense RLNC, which randomly selects all coding coefficients, and only for a statically fixed number of outer expansion packets. However, the probability that the coding coefficient row of a newly received packet is linearly independent of prior received coding coefficient rows (a prerequisite for successful decoding) is highly dynamic. We propose to exploit the dynamics of this probability to reduce the computational complexity of Fulcrum coding. In particular, we vary the density of non-zero coding coefficients, i.e., equivalently, the sparsity of coding coefficients, and the number of outer expansion packets to keep the complexity low while maintaining a reasonably high decoding probability. We introduce the general principles of dynamic sparsity and expansion packets (DSEP) for Fulcrum coding as well as two specific example DSEP policies. Our evaluations indicate that DSEP Fulcrum can increase the encoding throughput tenfold and increase the decoding throughput 1.4 to 4.3 fold while achieving decoding probabilities that are typically less than 1% lower than the conventional Fulcrum decoding probabilities. We also find that DSEP achieves somewhat higher encoding and decoding throughputs than the CodornicesRq (Release 2.1) implementation of RaptorQ block coding for small blocks (generations) of source packets, while RaptorQ is substantially faster for large generation sizes. Furthermore, we develop and evaluate an elementary DSEP recoding mechanism that achieves a recoding throughput more than double the decoding throughput.

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Immersive media services, such as augmented reality and virtual reality (AR/VR), a 360degree video, and free-viewpoint video (FVV), are popular today. They require massive data storage, ultrahigh computing power, and ultralow latency. It is hard to fulfill these requirements simultaneously in a conventional communication system using a cloud/centralized radio access network (C-RAN). Specifically, due to centralized processing in such a system, the end-to-end latency, as well as the burden on the fronthaul network, are expected to be high. Fog computing-based radio access networks (F-RAN), in contrast, have been widely considered as an enabler for immersive media. Our contribution in this paper is threefold: First, we propose various service scenarios reflecting the characteristics of immersive media. Second, we identify the technologies that are required to support the proposed service scenarios under F-RAN and discuss how they can support the proposed scenarios efficiently. Third, we discuss possible research opportunities.a A list of acronyms can be found in the Appendix.INDEX TERMS Fog computing, radio access networks, immersive media, free-viewpoint video, 360-degree video, virtual reality, augmented reality.

Massive content delivery is in the spotlight of the research community as both data traffic and the number of connected mobile devices are increasing at an incredibly fast pace. The enhanced mobile broadband (eMBB) is one of the main use cases for the fifth generation of mobile networks (5G), which focuses on transmitting greater amounts of data at higher data rates than in the previous generations, but also on increasing the area capacity (given in bits per second per square meter) and reliability. However, the broadcast and multicast implementation in 5G and presents several drawbacks such as unexpected disconnections and the lack of device-specific QoS guarantees. As a result, whenever the exact same content is to be delivered to numerous mobile devices simultaneously, this content must be replicated. Hence, the same number of parallel unicast sessions as users are needed. Therefore, novel systems that provide efficient massive content delivery and reduced energy consumption are needed. In this paper, we present a network-coded cooperation (NCC) protocol for efficient massive content delivery and the analytical model that describes its behavior. The NCC protocol combines the benefits of cooperative architectures known as mobile clouds (MCs) with Random Linear Network Coding (RLNC). Our results show the benefits of our NCC protocol when compared to the establishment of numerous parallel unicast sessions are threefold: offload data traffic from the cellular link, reduce the energy consumption at the cooperating users, and provide throughput gains when the cellular bandwidth is insufficient. INDEX TERMS Cooperation, fifth generation of mobile networks (5G), massive content delivery, random linear network coding (RLNC).

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Massive content delivery in cellular networks is in the spotlight of the research community as data traffic is increasing at an incredibly fast pace. The existing LTE-A implementation for content broadcast presents several issues such as indoor coverage, along with low energy and spectral efficiency. Therefore, novel systems that provide efficient massive content delivery and reduced energy consumption are needed. In this paper we present a massive content distribution protocol that combines the benefits of cooperative mobile clouds (CMCs) with Random Linear Network Coding (RLNC) through multicast WiFi links. Our main goal is to offload data traffic from the LTE-A link and to reduce the energy consumption at the cooperating UEs. We solve the problem of excessive signaling that oftentimes arises in cooperative approaches by eliminating feedback messages within the CMCs. Instead, we provide a simple but accurate analytic model to correctly configure the number of coded transmissions to be performed within the CMCs. Results show that energy savings of more than 37 percent can be achieved with our protocol when compared to direct content download from the cellular base station. Furthermore, bandwidth utilization at the LTE-A link is sharply reduced.