Pricing and Resource Allocation via Game Theory for a Small-Cell Video Caching System

Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the transmission latency whilst mitigating the redundant transmissions of popular videos over back-haul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VR), and mobile users (MU). The NSP leases its SBSs to the VRs for the purpose of making profits, and the VRs, after storing popular videos in the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. We conceive this system within the framework of Stackelberg game by treating the SBSs as a specific type of resources. We first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, based on the probability derived, we formulate a Stackelberg game to jointly maximize the average profit of both the NSP and the VRs. Also, we investigate the Stackelberg equilibrium by solving a non-convex optimization problem. With the aid of this game theoretic framework, we shed light on the relationship between four important factors: the optimal pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size of the SBSs, and the popularity distribution of the VRs. Monte-Carlo simulations show that our stochastic geometry-based analytical results closely match the empirical ones. Numerical results are also provided for quantifying the proposed game-theoretic framework by showing its efficiency on pricing and resource allocation.Comment: Accepted to appear in IEEE Journal on Selected Areas in Communications, special issue on Video Distribution over Future Interne

Fact sheet: Forest health and water supply protection project ballot measure: Exit poll results

On November 6, 2012, Flagstaff, Arizona voters overwhelmingly approved a $10 million bond - Question 405, Forest Health and Water Supply Protection Project - (hereafter the Watershed Project). Exit polling was conducted by Northern Arizona University researchers to understand underlying dimensions of voter support or opposition to payments for water resources

Probabilistic small-cell caching: performance analysis and optimization

Small-cell caching utilizes the embedded storage of small-cell base stations (SBSs) to store popular contents, for the sake of reducing duplicated content transmissions in networks and for offloading the data traffic from macro-cell base stations to SBSs. In this paper, we study a probabilistic small-cell caching strategy, where each SBS caches a subset of contents with a specific caching probability. We consider two kinds of network architectures: 1) the SBSs are always active, which is referred to as the always-on architecture, 2) the SBSs are activated on demand by mobile users (MUs), referred to as the dynamic on-off architecture. We focus our attention on the probability that MUs can successfully download contents from the storage of SBSs. First, we derive theoretical results of this successful download probability (SDP) using stochastic geometry theory. Then, we investigate the impact of the SBS parameters, such as the transmission power and deployment intensity on the SDP. Furthermore, we optimize the caching probabilities by maximizing the SDP based on our stochastic geometry analysis. The intrinsic amalgamation of optimization theory and stochastic geometry based analysis leads to our optimal caching strategy characterized by the resultant closed-form expressions. Our results show that in the always-on architecture, the optimal caching probabilities solely depend on the content request probabilities, while in the dynamic on-off architecture, they also relate to the MU-to-SBS intensity ratio. Interestingly, in both architectures, the optimal caching probabilities are linear functions of the square root of the content request probabilities. Monte-Carlo simulations validate our theoretical analysis and show that the proposed schemes relying on the optimal caching probabilities are capable of achieving substantial SDP improvement compared to the benchmark schemes

NARRATE: A Normal Assisted Free-View Portrait Stylizer

In this work, we propose NARRATE, a novel pipeline that enables simultaneously editing portrait lighting and perspective in a photorealistic manner. As a hybrid neural-physical face model, NARRATE leverages complementary benefits of geometry-aware generative approaches and normal-assisted physical face models. In a nutshell, NARRATE first inverts the input portrait to a coarse geometry and employs neural rendering to generate images resembling the input, as well as producing convincing pose changes. However, inversion step introduces mismatch, bringing low-quality images with less facial details. As such, we further estimate portrait normal to enhance the coarse geometry, creating a high-fidelity physical face model. In particular, we fuse the neural and physical renderings to compensate for the imperfect inversion, resulting in both realistic and view-consistent novel perspective images. In relighting stage, previous works focus on single view portrait relighting but ignoring consistency between different perspectives as well, leading unstable and inconsistent lighting effects for view changes. We extend Total Relighting to fix this problem by unifying its multi-view input normal maps with the physical face model. NARRATE conducts relighting with consistent normal maps, imposing cross-view constraints and exhibiting stable and coherent illumination effects. We experimentally demonstrate that NARRATE achieves more photorealistic, reliable results over prior works. We further bridge NARRATE with animation and style transfer tools, supporting pose change, light change, facial animation, and style transfer, either separately or in combination, all at a photographic quality. We showcase vivid free-view facial animations as well as 3D-aware relightable stylization, which help facilitate various AR/VR applications like virtual cinematography, 3D video conferencing, and post-production.Comment: 14 pages,13 figures https://youtu.be/mP4FV3evmy

Covert communication in relay and RIS networks

Covert communication aims to prevent the warden from detecting the presence of communications, i.e. with a negligible detection probability. When the distance between the transmitter and the legitimate receiver is large, large transmission power is needed, which in turn increases the detection probability. Relay is an effective technique to tackle this problem, and various relaying strategies have been proposed for long-distance covert communication in these years. In this article, we first offer a tutorial on the relaying strategies utilized in covert transmission. With the emergence of reflecting intelligent surface and its application in covert communications, we propose a hybrid relay-reflecting intelligent surface (HR-RIS)-assisted strategy to further enhance the performance of covert communications, which simultaneously improves the signal strength received at the legitimate receiver and degrades that at the warden relying on optimizing both the phase and the amplitude of the HR-RIS elements. The numerical results show that the proposed HR-RIS-assisted strategy significantly outperforms the conventional RIS-aided strategy in terms of covert rate