The evolution and deployment of fifth-generation (5G) and beyond (B5G) infrastructure will require a tremendous effort to specify the design, standards, and manufacturing. 5G is vital to modern technological evolution, including industry 4.0, automotive, entertainment, and health care. The ambitious and challenging 5G project is classified into three categories, which provide an essential supporting platform for applications associated with: Enhanced mobile broadband (eMBB) Ultra-reliable low-latency communication (URLLC) Massive machine-type communication (mMTC) The demand for URLLC grows, particularly for applications like autonomous guided vehicles (AGVs), unmanned aerial vehicles (UAVs), and factory automation, and has a strict requirement of low latency of 1 ms and high reliability of 99.999%. To meet the needs of communication-sensitive and computation-intensive applications with different quality-of-service (QoS) requirements, this evolution focuses on ultra-dense edge networks with multi-access edge computing (MEC) facilities. MEC emerges as a solution, placing resourceful servers closer to users. However, the dynamic nature of processing and interaction patterns necessitates effective network control, which is challenging due to stringent requirements on both communication and computation. In this context, we introduce a novel approach to optimally manage task offloading, considering the intricacies of heterogeneous computing and communication services. Unlike existing methods, our methodology incorporates the number of admitted service migrations and QoS upper and lower bounds as binding constraints. The comprehensive model encompasses agent positions, MEC servers, QoS requirements, edge network communication, and server computing capabilities. Formulated as a mixed-integer linear program (MILP), it provides an optimal schedule for service migrations and bandwidth allocation, addressing the challenges posed by computation-intensive and communication-sensitive applications. Moreover, tailoring to an indoor robotics environment, we explore optimization-based approaches seeking an optimal system-level architecture while considering QoS guarantees. Optimization tools, e.g., ARCHEX, prove their ability to capture cyber-physical systems (CPS) requirements and generate correct-by-construction architectural solutions. We propose an extension in ARCHEX by incorporating dynamic properties, i.e., robot trajectories, time dimension, application-specific QoS constraints, and finally, integrating the optimization tool with a discrete-event network simulator (OMNeT++). This extension automates the generation of configuration files and facilitates result analysis, ensuring a comprehensive evaluation. This part of the work focuses on the dynamism of robots, server-to-service mapping, and the integration of automated simulation. The proposed extension is validated by optimizing and analyzing various indoor robotics scenarios, emphasizing critical performance parameters such as overall throughput and end-to-end delay (E2E). This integrated approach addresses the complex interplay of computation and communication resources, providing a solution for dynamic mobility, traffic, and application patterns in edge server environments.

<p dir="ltr"> The general design goal of low latency communication systems is to minimize the end-to-end delay while attaining the predefined reliability and throughput requirements. The burgeoning demand for low latency communications motivates a renewed research interest of the tradeoff between delay, throughput, and reliability. In this dissertation research, we consider slotted-based systems and explore the potential advantages of the so-called sequential codes in low latency network communications.</p><p dir="ltr"> The first part of this dissertation analyzes the exact error probability of random linear streaming codes (RLSCs) in the large field size regime over the stochastic independently and identically distributed (i.i.d.) symbol erasure channels (SECs). A closed-form expression of the error probability <i>p</i>_<em>e</em> of large-field-size RLSCs is derived under, simultaneously, the finite memory length α and decoding deadline Δ constraints. The result is then used to examine the intricate tradeoff between memory length (complexity), decoding deadline (delay), code rate (throughput), and error probability (reliability). Numerical evaluation shows that under the same code rate and error probability requirements, the end-to-end delay of RLSCs is 40-48% of that of the optimal block codes (i.e., MDS codes). This implies that switching from block codes to streaming codes not only eliminates the queueing delay completely (which accounts for the initial 50% of the delay reduction) but also improves the reliability (which accounts for the additional 2-10% delay reduction).</p><p dir="ltr"> The second part of this dissertation focuses on the asymptotics of the error probability of RLSCs in the same system model of the first part. Two important scenarios are analyzed: (i) tradeoff between Δ and <i>p</i>_<em>e</em> under infinite α; and (ii) tradeoff between α and <i>p</i>_<em>e</em> under infinite Δ. In the first scenario, the asymptote of <i>p</i>_<em>e</em>(Δ) is shown to be <i>ρ</i>Δ^-1.5<i>e</i>^-^<em>η</em>^Δ. The asymptotic power term Δ^-1.5 of RLSCs is a strict improvement over the Δ^-0.5 term of random linear block codes. A pair of upper and lower bound on the asymptotic constant <i>ρ</i> is also derived, which are tight (i.e., identical) for one specific class of SECs. In the second scenario, a refine approximation is proposed by computing the parameters in a multiterm asymptotic form, which closely matches the exact error probability even for small memory length (≈ 20). The results of the asymptotics can be further exploited to find the <i>c</i>-optimal memory length <i>α</i>_<em>c</em>^*(Δ), which is defined as the minimal memory length α needed for the resulting <i>p</i>_<em>e</em> to be within a factor of <i>c</i>>1 of the best possible <i>p</i>_<em>e</em>^<em>*</em>_{<em> </em>}for any Δ, an important piece of information for practical implementation.</p><p dir="ltr"> Finally, we characterize the channel dispersions of RLSCs and MDS block codes, respectively. New techniques are developed to quantify the channel dispersion of sequential (non-block-based) coding, the first in the literature. The channel dispersion expressions are then used to compare the levels of error protection between RLSCs and MDS block codes. The results show that if and only if the target error probability <i>p</i>_<em>e</em> is smaller than a threshold (≈ 0.1774), RLSCs offer strictly stronger error protection than MDS block codes, which is on top of the already significant 50% latency savings of RLSCs that eliminate the queueing delay completely.</p>