Cellular networks have evolved to support high peak bitrates with low loss rates as observed by the higherlayers. However, applications and services running over cellular networks are now facing other difficult congestion-related challenges, most notably a highly variable link capacity and bufferbloat. To overcome theseissues and improve performance of network traffic in 4G/5G cellular networks, a number of in-network and end-to-end solutions have been proposed. Fairness between interacting congestion control algorithms (CCAs) has played an important role in the type of CCAs considered for research and deployment. The placement of content closer to the user and the allocation of per-user queues in cellular networks has increased the likelihood of a cellular access bottleneck and reduced the extent of flow interaction between multiple users. This has resulted in renewed interest in end-to-end CCAs for cellular networks by opening up room for researchand exploration. In this work, we present end-to-end CCAs that target a high throughput and a low latency over highly variable network links, and classify them according to the way they address the congestion control. The work also discusses the deployability of the algorithms. In addition, we provide insights into possible future research directions, such as coping with a higher degree of variability, interaction of CCAs in as hared bottleneck, and avenues for synergized research, such as CCAs assisted by software defined networking and network function virtualization. We hope that this work will serve as a starting point for systematically navigating through the expanding number of cellular CCAs.

An increasing number of cellular congestion controlalgorithms (CCAs) are relying on measurements of the deliveryrate observed at the receiver. Accordingly, early detection ofchanges in the receiver’s rate would improve the performanceof such algorithms. Rate measurements over short time intervalscould allow fast detection of change in the rate observed bythe upper layers of a cellular receiver. However, for cellularreceivers, upper-layer rate measurements over short time scalesproduce unreliable results due to the effect of underlying lowerlayer mechanisms such as scheduling and retransmissions. In thispaper, we introduce a rate estimation approach that reduces thevariability observed in short time scale receiver rate measurementsand allows faster rate change detection. We also integratean adaptive mechanism to improve online measurements overdifferent time scales.

Cellular networks are continuously evolving to allow improved throughput and low latency performance for applications. However, it has been shown that, due to buffer over-provisioning, TCP’s standard loss-based congestion control algorithms (CCAs) can cause long delays in cellular networks. The QUIC transport protocol and the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control are both proposed in response to shortcomings observed in TCP and loss-based CCAs. Despite its notable advantages, BBR can experience suboptimal delay performance in cellular networks due to one of its underlying design choices: the maximum bandwidth filter at the sender. In this work, we leverage QUIC’s extensibility to enhance BBR. Instead of using the ACK rate observed at the sender side, we apply a more fitting delivery rate calculated at the receiver. Our 5G-trace-based emulation experiments in CloudLab suggest that our modified QUIC could significantly improve latency without any notable effect on the throughput: In particular, in some of our experiments, we observe up to 39% reduction of the round-trip time (RTT) with a worst case throughput reduction of 2.7%.

BBR is a promising new congestion control algorithm (CCA) that has been shown to result in significantly lower latency compared to conventional loss-based CCAs. However, in cellular networks, where there is a high variability in the available rate, BBR does not perform as well as expected. In such scenarios, BBR tends to overestimate the available capacity and create queues that cause longer packet delays. In this work, we propose Receiver-driven BBR (RBBR), a modified version of BBR that uses rate estimates made at the receiver side rather than at the sender side. We employ a Kalman filter to make a more accurate estimate of the available bandwidth, and we implement the algorithm in QUIC. An evaluation of the proposed CCA is done through extensive 4G trace-based emulations, real 4G network tests and mmWave trace-based emulations representing a 5G scenario. The results show that RBBR is able to achieve an RTT reduction of up to 80\% with a worst-case throughput loss of about 30\%. The results also show that in real 4G networks, RBBR flows experience a more predictable and consistent RTT than what BBR flows do.

The deployment of the next generation of cellular networks (5G) is expanding to enable new services and improve the quality of existing ones. Despite the benefits of 5G networks, they also present new challenges for the performance of classical and recently-proposed congestion control algorithms (CCAs), e.g., Bottleneck bandwidth and round-trip propagation time (BBR) and Copa. Despite their successful adoption in the Internet, BBR has been shown to overestimate the bottleneck bandwidth in cellular networks, and Copa has not been independently tested on a similar scale and detail as BBR in cellular networks. In this work, we compare the performance of these fairly recent CCAs as well as the widely deployed CUBIC CCA, and a modification to BBR for cellular networks (RBBR) at 5G rates. The evaluation is performed using the emerging QUIC protocol and uses both emulations and live experiments. Our results show that in 5G networks, CUBIC, BBR, and Copa suffer from significant bufferbloat, longer packet delays, and lower throughput, respectively. We also observe that in cases where the bottleneck is largely in the 5G link, RBBR can offer a significant delay reduction compared to BBR and CUBIC. 

The lack of consideration for application delay requirements in standard loss-based congestion control algorithms (CCAs) has motivated the proposal of several alternative CCAs. As such, Copa is one of the most recent and promising CCAs, and it has attracted attention from both academia andindustry. The delay performance of Copa is governed by amostly static latency-throughput tradeoff parameter, δ. However,a static δ parameter makes it difficult for Copa to achieve consistent delay and throughput over a range of bottleneck bandwidths. In particular, the coexistence of 4G and 5G networks and the wide range of bandwidths experienced in NG-RANs can result in inconsistent CCA performance. To this end, we propose a modification to Copa, Copa-D, that dynamically tunes δ to achieve a consistent delay performance. We evaluate the modification over emulated fixed, 4G, and 5G bottlenecks. The results show that Copa-D achieves consistent delay with minimal impact on throughput in fixed capacity bottlenecks. Copa-D also allows a more intuitive way of specifying the latency-throughput tradeoff and achieves more accurate and predictable delay invariable cellular bottleneck.