We present NIER, a video conferencing system that can adaptively maintain a low bitrate (e.g., 10–100 Kbps) with reasonable visual quality while being robust to packet losses. We use key-point-based deep image animation (DIA) as a key building block and address a series of networking and system challenges to make NIER practical. Our evaluations show that NIER significantly outperforms the baseline solutions.

Mobile video streaming services have widely adopted Adaptive Bitrate (ABR) streaming to dynamically adapt the streaming quality to variable network conditions. A wide range of third-party entities such as network providers and testing services need to understand such adaptation behavior for purposes such as QoE monitoring and network management. The traditional approach involved conducting test runs and analyzing the HTTP-level information from the associated network traffic to understand the adaptation behavior under different network conditions. However, end-to-end traffic encryption protocols such as HTTPS and QUIC are being increasingly used by streaming services, hindering such traditional traffic analysis approaches.

Analyzing delivered QoE for Adaptive Bitrate (ABR) streaming over cellular networks is critical for a host of entities including content providers and mobile network providers. However, existing approaches mostly rely on network traffic analysis. In addition to potential accuracy issues, they are challenged by the increasing use of end-to-end network traffic encryption. In this paper, we explore a very different approach to QoE measurement --- utilizing the screen recording capability widely available on commodity devices to record the video displayed on the mobile device screen, and analyzing the recorded video to measure the delivered QoE. We design a novel system VideoEye to conduct such screen-recording-based QoE analysis. We identify the various technical challenges involved, including distortions introduced by the screen recording process that can make such analysis difficult. We develop techniques to accurately measure video QoE from the screen recordings even in the presence of recording distortions. Our evaluations demonstrate that VideoEye accurately detects important QoE indicators including the track played at different points in time, and stall statistics. The maximal error in detected stall duration is 0.5 s. The accuracy of detecting the displayed tracks is higher than 97%.

HTTP Adaptive Streaming (HAS) has emerged as the predominant technique for transmitting video over cellular for most content providers today. While mobile video streaming is extremely popular, delivering good streaming experience over cellular networks is technically very challenging, and involves complex interacting factors. We conduct a detailed measurement study of a wide cross-section of popular streaming video-on-demand (VOD) services to develop a holistic understanding of these services' design and performance. We identify performance issues and develop effective practical best practice solutions to mitigate these challenges. By extending the understanding of how different, potentially interacting components of service design impact performance, our findings can help developers build streaming services with better performance.

Mobile Internet availability, performance and reliability have remained stubbornly opaque since the rise of cellular data access. Conducting network measurements can give us insight into user-perceived network conditions, but doing so requires careful consideration of device state and efficient use of scarce resources. Existing approaches address these concerns in ad-hoc ways.

No abstract available.

While the availability and accessibility of cellular network connectivity have improved in recent years, our ability to diagnose and debug network problems in this environment has not. One key challenge is that many of the network problems occur near the edge of the network where only mobile devices can perceive them, but network and battery resources to conduct measurements from these mobile devices are scarce. Traditional network measurement approaches that use continuous, periodic, or random measurements are either infeasible or ineffective in this environment.