With the growing demand for multimedia services, Dynamic Adaptive Streaming over HTTP (DASH) has become a key solution for delivering high-quality video content. In this work, we consider an Edge-DASH scenario and formulate a joint optimization problem that involves four critical aspects: bitrate allocation, user-to-server assignment, caching, and bandwidth allocation. Due to the complexity of the joint problem, we decompose it into sub-problems and address them separately. To solve the resulting sub-problems, we employ deep reinforcement learning, specifically the Deep Deterministic Policy Gradient (DDPG) method, for three of them, and develop a heuristic solution for the fourth. Simulation results demonstrate that our approach enhances performance across multiple metrics, including improved video delivery, reduced buffer underflow and overflow, and more efficient caching, which collectively enable greater utilization of edge resources for streaming. Moreover, we evaluated inference latency across edge and cloud hardware, confirming sub- to few-millisecond performance suitable for real-time deployment. This showcases the benefits of combining learning-based and heuristic techniques to meet the growing demand for adaptive video streaming in edge computing environments.

In additive manufacturing (AM), factories can quickly adjust their production to meet dynamic object demand without losing profitability, which allows them to implement less complex and more efficient supply chains. To facilitate the adoption of simplified supply chains, we propose the AM Cloud, where a set of micro-manufacturers pool their resources and offer them in an on-demand and pay-per-use basis. However, managing the AM Cloud requires setting a price for objects that incentivizes demand while guaranteeing that the manufacturers can earn a profit. It also requires finding control decisions that minimize operating costs under uncertain object demand. To address these challenges, we first propose a randomized multiobject auction that allocates printing area to the winning buyers and sets prices. We then design an online dynamic control algorithm that finds optimal decisions for the manufacturers to fulfill the object production orders from the auction winners without detailed knowledge of the system statistics. We show that the auction mechanism is truthful, individually rational, and guarantees that the manufacturers can make a profit. We also show that the solution obtained by our dynamic control algorithm is within a tight bound of the optimal one and can fulfill orders within a buyer-defined deadline.

In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.