Network fluctuations can cause unpredictable degradation of the user’s quality of experience (QoE) on real-time video streaming. The intrinsic property of real-time video streaming, which generates delay-sensitive and chunk-based video frames, makes the situation even more complicated. Although previous approaches have tried to alleviate this problem by controlling the video bitrate based on the current network capacity estimate, they do not take into account the explicit queueing delay experienced by the video frame in determining the bitrate of upcoming video frames. To tackle this problem, we propose a new real-time video streaming system, Exstream, that can adapt to dynamic network conditions with the help of video bitrate control method and bandwidth estimation method designed to support real-time video streaming environments. Exstream explicitly estimates the queueing delay experienced by the video frame based on the transmission time budget that each frame can maximally utilize, which depends on the frame generation interval, and adjusts the bitrate of newly generated video frames to suppress the queueing delay level close to zero. Our comprehensive experiments demonstrate that Exstream achieves lower frame delay than four existing systems, Salsify, WebRTC, Skype, and Hangouts without frequent video frame skip.

In this paper, we conduct a measurement study on operational 5G networks deployed across different frequency bands (mmWave and sub-6GHz) and server locations (mobile edge and Internet cloud). Specifically, we assess 5G performance in both uplink and downlink across multiple operators’ networks. We then carry out extensive comparisons of transport-layer protocols using ten different algorithms in full-fledged 5G networks, including an edge computing environment. Finally, we evaluate representative mobile applications over the 5G network with and without edge servers. Our comprehensive measurements provide several insights that affect the experience of 5G users: (i) With a 5G edge server, existing TCP congestion control algorithms can achieve throughput up to 1.8Gbps with only a single flow. (ii) The maximum TCP receive buffer size, which is set by off-the-shelf 5G phones, can limit the throughput performance of 5G networks, which is not observed in 4G LTE-A networks. (iii) Despite significant latency gains in download-centric applications, the 5G edge service provides limited benefits to CPU-intensive tasks or those that use significant uplink bandwidth. To our knowledge, this is the first measurement-driven understanding of 5G edge computing “in the wild,” which can provide an answer to how edge computing would perform in real 5G networks.

As video calls and personal broadcasting become popular, the demand for mobile live streaming over cellular uplink channels is growing fast. However, current live streaming solutions are known to suffer from frequent uplink throughput fluctuations causing unnecessary video stalls and quality drops. As a remedy to this problem, we propose PERCEIVE, a deep learning-based uplink throughput prediction framework. PERCEIVE exploits a 2-stage LSTM (Long Short Term Memory) design and makes throughput predictions for the next 100ms. Our extensive evaluations show that PERCEIVE, trained with LTE network traces from three major operators in the U.S., achieves high accuracy in the uplink throughput prediction with only 7.67% mean absolute error and outperforms existing prediction techniques. We integrate PERCEIVE with WebRTC, a popular video streaming platform from Google, as a rate adaptation module. Our implementation on the Android phone demonstrates that it can improve PSNR by up to 6dB (4x) over the default WebRTC while providing less streaming latency.