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.

Neural-enhanced video streaming (e.g., super-resolution) is an ongoing revolution which can provide extremely high-quality video streaming services breaking the restriction of bandwidth. However, such enhancements require intense computation power that is not affordable for a single mobile device, which hinders their real-world deployment. To address the limitation, we propose OASIS, the first system that facilitates multiple users in close proximity to execute intense neural-enhanced video streaming in realtime. To this end, OASIS intelligently distributes computation tasks among multiple mobile devices, selects appropriate video bitrates and super-resolution models, and optimizes video chunk delivery. As a result, the expensive neural-enhanced streaming is done through distributed collaboration, achieving optimal quality of experience (QoE). We implement and evaluate OASIS on commodity smartphones from different vendors, under various network and computation conditions. Extensive experiments demonstrate the high efficiency of OASIS: it improves the video streaming QoE by 40%-200% and reduces each participant's energy consumption by 60% when the system scales up from a single device to six devices.

By aggregating multiple channels, Carrier Aggregation (CA) is an important technology for boosting cellular network bandwidth. Given diverse radio bands made available in 5G networks, CA plays a particularly critical role in achieving the goal of multi-Gbps throughput performance. In this paper, we carry out a timely comprehensive measurement study of CA deployment in commercial 5G networks (as well as 4G networks). We identify the key factors that influence whether CA is deployed and when, as well as which band combinations are used. Thus, we reveal the challenges posed by CA in 5G performance analysis and prediction as well as their implications in application quality-of-experience (QoE). We argue for and develop a novel CA-aware deep learning framework, dubbed Prism5G, which explicitly accounts for the complexity introduced by CA to more effectively predict 5G network throughput performance. Through extensive evaluations, we demonstrate the superiority of Prism5G over existing throughput prediction algorithms. Prism5G improves 5G throughput prediction accuracy by over 14% on average and a maximum of 22%. Using two use cases as examples, we further illustrate how Prism5G can aid applications in optimizing QoE performance.

QUIC is expected to be a game-changer in improving web application performance. In this paper, we conduct a systematic examination of QUIC's performance over high-speed networks. We find that over fast Internet, the UDP+QUIC+HTTP/3 stack suffers a data rate reduction of up to 45.2% compared to the TCP+TLS+HTTP/2 counterpart. Moreover, the performance gap between QUIC and HTTP/2 grows as the underlying bandwidth increases. We observe this issue on lightweight data transfer clients and major web browsers (Chrome, Edge, Firefox, Opera), on different hosts (desktop, mobile), and over diverse networks (wired broadband, cellular). It affects not only file transfers, but also various applications such as video streaming (up to 9.8% video bitrate reduction) and web browsing. Through rigorous packet trace analysis and kernel- and user-space profiling, we identify the root cause to be high receiver-side processing overhead, in particular, excessive data packets and QUIC's user-space ACKs. We make concrete recommendations for mitigating the observed performance issues.

QUIC is a multiplexed transport-layer protocol over UDP and comes with enforced encryption. It is expected to be a game-changer in improving web application performance. Together with the network layer and layers below, UDP, QUIC, and HTTP/3 form a new protocol stack for future network communication, whose current counterpart is TCP, TLS, and HTTP/2. In this study, to understand QUIC's performance over high-speed networks and its potential to replace the TCP stack, we carry out a series of experiments to compare the UDP+QUIC+HTTP/3 (QUIC) stack and the TCP+TLS+HTTP/2 (HTTP/2) stack. Preliminary measurements on file download reveal that QUIC suffers from a data rate reduction compared to HTTP/2 across different hosts.

5G-NR is beginning to be widely deployed in the mmWave frequencies in urban areas in the US and around the world. Due to the directional nature of mmWave signal propagation, improving performance of such deployments heavily relies on beam management and deployment configurations. We perform detailed measurements of mmWave 5G deployments by two major commercial 5G operators in the US in two diverse environments: an open field with a baseball park (BP) and a downtown urban canyon region (DT), using smartphone-based tools that collect detailed measurements across several layers (PHY, MAC and up) such as beam-specific metrics like signal strength, beam switch times, and throughput per beam. Our measurement analysis shows that the parameters of the two deployments differ in a number of aspects: number of beams used, number of channels aggregated, and density of deployments, which reflect on the throughput performance. Our measurement-driven propagation analysis demonstrates that narrower beams experience a lower path-loss exponent than wider beams, which combined with up to eight frequency channels aggregated on up to eight beams can deliver a peak throughput of 1.2 Gbps at distances greater than 100m.

With 5G's support for diverse radio bands and different deployment modes, e.g., standalone (SA) vs. non-standalone (NSA), mobility management - especially the handover process - becomes far more complex. Measurement studies have shown that frequent handovers cause wild fluctuations in 5G throughput, and worst, service outages. Through a cross-country (6,200 km+) driving trip, we conduct in-depth measurements to study the current 5G mobility management practices adopted by three major U.S. carriers. Using this rich dataset, we carry out a systematic analysis to uncover the handover mechanisms employed by 5G carriers, and compare them along several dimensions such as (4G vs. 5G) radio technologies, radio (low-, mid- & high-)bands, and deployment (SA vs. NSA) modes. We further quantify the impact of mobility on application performance, power consumption, and signaling overheads. We identify key challenges facing today's NSA 5G deployments which result in unnecessary handovers and reduced coverage. Finally, we design a holistic handover prediction system Prognos and demonstrate its ability to improve QoE for two 5G applications 16K panoramic VoD and realtime volumetric video streaming. We have released the artifacts of our study at https://github.com/SIGCOMM22-5GMobility/artifact.

Motivated by the rapid deployment of 5G, we carry out an in-depth measurement study of the performance, power consumption, and application quality-of-experience (QoE) of commercial 5G networks in the wild. We examine different 5G carriers, deployment schemes (Non-Standalone, NSA vs. Standalone, SA), radio bands (mmWave and sub 6-GHz), protocol configurations (_e.g._ Radio Resource Control state transitions), mobility patterns (stationary, walking, driving), client devices (_i.e._ User Equipment), and upper-layer applications (file download, video streaming, and web browsing). Our findings reveal key characteristics of commercial 5G in terms of throughput, latency, handover behaviors, radio state transitions, and radio power consumption under the above diverse scenarios, with detailed comparisons to 4G/LTE networks. Furthermore, our study provides key insights into how upper-layer applications should best utilize 5G by balancing the critical tradeoff between performance and energy consumption, as well as by taking into account the availability of both network and computation resources. We have released the datasets and tools of our study at https://github.com/SIGCOMM21-5G/artifact.