Art exhibit highlighting creative the creative talent of UConn Health Employees from across the organization.

"Expressions in Multimedia" by Martha G. Trask "Secondary Effects" by Jeff Ostergren Join us for a reception Thursday, May 22, from 4 to 6 p.m. in the Celeste LeWitt Gallery. (north side of the food court)Martha G. Trask is an expressive mixed media artist who happens to work in our library.Jeff Ostergren infuses his paint with actual medications to tell stories about the intertwined histories of pharmaceuticals and color.

UConn Health Day 2 new employee orientation is conducted on Saba. It focuses on comprehensive training for our newest workforce, covering diversity awareness, sexual harassment prevention, and compliance to ensure a respectful and compliant work environment.AgendaDay 2Format: Saba Self-Guided Learning Time: 8 am - 4:30 pm Location: RemoteActivities: Dive deeper into your compliance and role-specific training and explore resources at your own pace.

Abstract: Adaptive bitrate (ABR) streaming is the current de facto video streaming technology in the Internet. It accounts for the majority of traffic in cellular networks, and also places a heavy demand on users' limited monthly cellular data budgets. Despite much research on ABR streaming in cellular networks, reducing data usage and improving quality of experience (QoE) remain a significant challenge. In this dissertation, we explore two directions in resolving the challenge: (1) developing quality-aware ABR streaming strategies guided by data budget to reduce data usage, while minimizing the impact on QoE, and (2) developing accurate cross-layer bandwidth estimation for low-latency live (LLL) ABR streaming to improve QoE for end users. In the first part of the dissertation, we develop an approach, DataPlanner, that uses data budget information to better manage the data usage of mobile video streaming, while minimizing the impact on users' QoE. Specifically, we propose a novel framework for quality-aware ABR streaming involving a per-session data budget constraint. Under the framework, we develop two planning-based strategies, one for the case where fine-grained perceptual quality information is known to the planning scheme, and another for the case where such information is not available. Using evaluations under a wide range of network conditions using different videos covering a variety of content types and encodings, we demonstrate that both strategies use much less data compared to state-of-the-art ABR schemes, while still providing comparable QoE.   In the second part, we propose a cross-layer bandwidth estimation (CLBE) approach that combines coarse-grained application-level semantics with fine-grained kernel-level packet capture to achieve higher accuracy. We incorporate CLBE into three open-source ABR players and show that it provides significantly more accurate bandwidth estimation than state-of-the-art application-level techniques. This accuracy leads to better bandwidth prediction and notably improved QoE for end users in low-latency live streaming scenarios. Together, these contributions advance the field of video streaming by enabling data-efficient and QoE-aware adaptive streaming in dynamic cellular environments, offering practical enhancements for both bandwidth-constrained users and real-time streaming systems.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).