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Online video is anticipated to be the largest fraction of all mobile network traffic aside from the huge processing tasks imposed on networks by the billions of IoT devices, causing unprecedented challenges to the current network architecture. Edge caching has been proposed as a highly promising technology to overcome this challenge by placing computational and data storage resources at the network edge to reduce latency and backhaul traffic. However, the edge resources are heavily constrained in their storage and computational capacities as large-scale deployments mean fairly distributing resources across the network. Addressing this limitation, we propose an edge video caching scheme that dynamically caches the first part of popularity-ranked video files on Multi-Edge Computing Access Node (MAN) servers envisioned to achieve higher cache hit ratios, lower latencies, and lower backhaul traffic. The concept of Regionally Organized Clouds (ROCs) with sufficient resources for file caching and compute-intensive tasks was introduced, and a formulation of the edge caching problem as an Integer Linear Programming (ILP) problem was made. Additionally, this study proposes a file view-time threshold for each cached video aimed at reducing the resource wastage caused when buffered contents are abandoned. Comparative evaluations of the proposed show its excellent performance over FIFO, Greedy, LFRU and TLRU schemes.

During wireless video transmission, channel conditions can vary drastically. When the channel fails to support the transmission bit rate, the video quality degrades sharply. A pseudo-analog transmission system such as SoftCast relies on linear operations to achieve a linear quality transition over a wide range of channel conditions. When transmitting 3D videos over SoftCast, the following issues arise: (1) assigning the transmission power to texture and depth maps to obtain the optimal overall quality and (2) handling 3D video data traffic by dropping and re-allocating resources. This paper solves the pseudo-analog transmission resource allocation problem and improves the results by applying the optimal joint power allocation. First, the minimum and the target distortion optimization problems are formulated in terms of a power–bandwidth pair versus distortion. Then, a minimum distortion optimization algorithm iteratively computes all the possible resource allocations to find the optimal allocation based on the minimum distortion. Next, the three-dimensional target distortion problem is divided into two subproblems. In the power-distortion problem, to obtain a target distortion, the algorithm exhaustively solves the closed form of the power resource under a predefined upper-bound bandwidth. For the bandwidth-distortion problem, reaching a target distortion requires solving iteratively for the bandwidth resource closed form, given a predefined power. The proposed resource control scheme shows an improvement in transmission efficiency and resource utilization. At low power usage, the proposed method could achieve a PSNR gain of up to 1.5 dB over SoftCast and even a 1.789 dB gain over a distortion-resource algorithm, using less than 1.4% of the bandwidth.

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