The increasing demand for high-quality video streaming over Wireless Local Area Networks (WLAN) presents significant challenges in terms of bandwidth utilization, latency, and overall network performance. This study investigates effective optimization strategies to enhance video transmission in WLAN environments. Key areas of focus include adaptive bitrate streaming, Quality of Service (QoS) protocols, error correction techniques, and network congestion management. By integrating these approaches, we aim to improve video quality, reduce buffering, and enhance user experience. The research involves a comprehensive analysis of existing optimization techniques, simulation of various network scenarios, and evaluation of their impact on video transmission efficiency. Our findings provide actionable insights for network engineers and IT professionals seeking to optimize video delivery in WLAN settings, ensuring robust and reliable video streaming performance.

The demand for enhanced wireless communication capabilities, driven by visions for 6G and the evolution of 5G networks, necessitates innovative approaches to overcome the limitations of existing infrastructure [1, 4]. Traditional methods, such as scaling up MIMO systems, face significant challenges related to hardware complexity and power consumption [6, 7]. Reconfigurable Intelligent Surfaces (RIS), also known as Intelligent Reflecting Surfaces (IRS), have emerged as a promising paradigm to reshape the wireless environment passively and smartly [2, 3, 16, 25]. An RIS is a planar surface composed of numerous passive reflecting elements, each capable of independently altering the phase and/or amplitude of an incident electromagnetic wave [2, 3, 16]. By controlling these elements, the RIS can collectively steer reflected signals towards desired receivers, effectively creating favorable propagation conditions and mitigating detrimental effects like fading and interference [2, 3, 18, 19]. This article reviews key approaches for enhancing wireless communication performance using RIS and discusses methodologies for evaluating these performance gains. We explore the fundamental principles of RIS-aided communication, various techniques for joint optimization of active and passive beamforming, power control, and diversity [14, 18, 19, 20, 21, 22, 23, 24]. Furthermore, we examine the crucial role of performance evaluation metrics and simulation/analytical techniques in quantifying the benefits of RIS-assisted systems [14, 27, 28, 29]. Drawing upon recent literature [1-29], this review highlights the potential of RIS to significantly improve metrics such as signal-to-noise ratio (SNR), coverage, energy efficiency, and diversity, paving the way for more robust and efficient future wireless networks.

Finite Impulse Response (FIR) filters are integral to modern digital signal processing applications requiring high throughput and low latency. However, implementing high-order FIR filters on Field Programmable Gate Arrays (FPGAs) remains challenging due to significant resource utilization and power consumption, especially when targeting real-time performance. This study presents an accelerated FIR filter design that leverages specialized compressor architectures and optimized approximate adders to balance speed, area, and energy efficiency. By integrating compressor-based partial product reduction with approximate arithmetic techniques, the proposed architecture achieves substantial improvements in computational latency and logic utilization compared to conventional designs. Synthesis and implementation results on a Xilinx FPGA platform demonstrate that the design reduces critical path delay and resource consumption by up to 35%, while maintaining acceptable error tolerances for many signal processing workloads. These findings underscore the potential of approximate computing combined with customized compressor structures to enable efficient high-performance FIR filtering on reconfigurable hardware.

Purpose: This study addresses the limitations of conventional Site Reliability Engineering (SRE) observability, which often relies on fragmented, reactive monitoring across logs, metrics, and traces. We propose and validate a novel Unified Telemetry and Predictive Modeling Framework (UT-PMF) designed to consolidate these data streams and provide proactive health signals.

Methodology: The UT-PMF integrates a Unified Telemetry Framework (UTF) for data ingestion and correlation with a Predictive Modeling Module (PMM). The PMM employs time-series anomaly detection for metrics and a Transformer-based Natural Language Processing (NLP) model for log pattern change detection. The framework was evaluated in a simulated, distributed system against a baseline reactive monitoring setup, using Mean Time to Detection (MTTD) and Service Level Objective (SLO) compliance as primary metrics.

Findings: The implementation of the UT-PMF yielded a substantial improvement in incident response, demonstrating a 45% reduction in MTTD compared to the baseline. The predictive fusion of metric anomalies and log precursors allowed SRE teams to identify and address latent system degradation significantly earlier. This proactive capability directly supports improved SLO attainment and error budget management.

Originality: This research contributes an integrated architectural and algorithmic approach that moves beyond mere data collection to unified, cross-pillar predictive analysis, offering a transformative path for SRE observability in complex, high-stakes production environments.