text: In this paper, we revisit the idea to remove Slow Start from congestion control. To do so, we build upon the newly gained freedom of transport protocol extendability offered by QUIC to hint bandwidth estimates from a typical web client to a server. Using this bandwidth estimate, we bootstrap congestion windows of new connections to quickly utilize available bandwidth. This custom flow initialization removes the common early exit of Slow Start and thus fuels short flow fairness with long-running connections. Our results indicate that we can drastically reduce flow completion time accepting some losses and thereby an inflated transmission volume. For example, for a typical DSL client, loading a 2 MB YouTube video chunk is accelerated by nearly 2x. In the worst case, we find an inflation of the transfer volume by 12 due to losses.
  @cite_24: In this paper we propose a stochastic model to predict user throughput in mobile networks. In particular, the model accounts for uncertainty such as random phenomena (e.g., fast fading) or inexact information (e.g., user location) to derive the statistical distribution of the user throughput. Such a model is highly useful for aiding scheduling and resource allocation decisions. In addition, we provide a taxonomy of prediction techniques to investigate error sources and the main characteristics of prediction accuracy. Finally, we show the versatility of the model by analyzing LTE user throughput for the case where knowledge of either the user's actual position or the congestion level in the cell is inexact.
  @cite_1: Throughput has a strong impact on user experience in cellular networks. The ability to predict the throughput of a connection, before it starts, will bring new possibilities, particularly to the Internet service providers. They could adapt contents to the quality of service really reachable by users, in order to enhance their experience. First this study highlights the prediction capabilities thanks to different algorithms and data gathered at different network levels. Then we propose a simple approach based on machine learning to predict the throughput using a few data related to the context of use.
  @cite_7: With the advent of high-speed cellular access and the overwhelming popularity of smartphones, a large percent of today's Internet content is being delivered via cellular links. Due to the nature of long-range wireless signal propagation, the capacity of the last hop cellular link can vary by orders of magnitude within a short period of time (e.g., a few seconds). Unfortunately, TCP does not perform well in such fast-changing environments, potentially leading to poor spectrum utilization and high end-to-end packet delay. In this paper we revisit seminal work in cross-layer optimization in the context of 4G cellular networks. Specifically, we leverage the rich physical layer information exchanged between base stations (NodeB) and mobile phones (UE) to predict the capacity of the underlying cellular link, and propose nCQIC, a cross-layer congestion control design. Experiments on real cellular networks confirm that our capacity estimation method is both accurate and precise. A CQIC sender uses these capacity estimates to adjust its packet sending behavior. Our preliminary evaluation reveals that CQIC improves throughput over TCP by 1.08-2.89x for small and medium flows. For large flows, CQIC attains throughput comparable to TCP while reducing the average RTT by 2.38-2.65x.
summary: While past connections can hint at receiver network conditions, we rather see them as a building block that can be used in combination with a receiver-side bandwidth hint. In our work, we highlighted possible angles in which to tackle bandwidth estimates but do not go into detail. @cite_7 shows how to utilize cellular (4G) PHY-layer information at the client for bandwidth estimations, they even implement it in the Google QUIC framework but do not show the effects on other flows, fairness and losses nor focus on the startup phase. Further bandwidth estimations @cite_1 @cite_24 utilize client context information ( location, signal quality, speed) in cellular networks to learn and predict channel throughput, which could be used in an implementation of our system.