CTN Issue: July 2023

A note from the editor:

Congestion control has been a fundamental function in computer networks as it allows controlling the throughput of the data injected into the network with the goal of reducing the impact of congestion on applications’ performance. However, congestion control has been a daunting challenge, especially in wireless environments, and this is due to their inherent nature of incurring unpredictable performance shifts. This becomes even more challenging with the emergence of eXtended Reality (XR) applications and their stringent performance requirements in terms of latency, throughput and tolerance to packet loss.

In this month's article, the authors provide us with an overview of the key challenges related to congestion control in computer networks in the particular context of wireless environments and XR applications. They also shed light on key research directions and potential solution avenues to design congestion control schemes that are better adapted to wireless environments and XR applications. It is clear that with the growing need for Ultra Reliable and Low Latency Communications, congestion control will undoubtedly continue to be a major challenge and research topic for the future! We hope you enjoy this enlightening article and the key research directions highlighted therein.

Miguel Dajer, CTN Editor-in-Chief

Introduction

Through the past three decades, a large body of work has been devoted to Congestion Control (CC) in computer networks. Indeed, congestion control has a major impact on the network performance in terms of throughput, packet loss and delays, which directly affects applications' performance and users’ quality of experience. Unsurprisingly, a large array of congestion control schemes has been developed in an attempt to smartly adjust data rates originating from the sources and to have better control on the performance metrics while taking into account various network conditions and considering different types of applications. IT giants and working groups (e.g., IETF RMCAT) had an increasing interest in congestion control and put forward several CC schemes (e.g., BBR [1], Copa [2], SCReAM [3], NADA [4], GCC [5], and LEDBAT [6]) adapted to different objectives and currently adopted to deliver widely popular applications from the likes of YouTube, Alphabet (Google), Meta (Facebook) and Microsoft. Despite these efforts, ensuring efficient congestion control is still a daunting challenge, especially with the increasing number of user devices and flows, the growing reliance on wireless and mobile technologies, and the stringent performance requirements of  applications that are currently gaining momentum like real-time interactive media transmission and eXtended Reality (XR) applications (e.g., virtual reality, augmented reality, holoportation, telepresence).

The goal of this article is two-fold: (1) identify the challenges of congestion control considering the characteristics of wireless environments (e.g., mobility, handover, non-congestive loss and delay) and XR applications (e.g., tight performance requirements), (2) we shed light on key research directions and potential solutions to design congestion control that is better adapted to wireless environments and XR applications.

Challenges of Congestion Control for XR Applications and Wireless Environments

In this Section, we discuss the challenges brought by the characteristics of wireless environments and XR/real-time applications to the congestion control problem. These challenges are summarized as follows:

• Non-congestive loss and delay in wireless links: unlike wired networks, non-congestive loss and delay may occur frequently in wireless networks, i.e., packets may be lost even when the network is not congested. Such loss may occur for several reasons like host mobility, handovers and random bit errors due to interference, noise, shadowing effect, and fast fading in the wireless media [7]. Non‑congestive loss may mislead CC decisions and force it to reduce the sending rate when no real congestion is occurring. In addition, wireless links are known to incur an additional non‑congestive delay that could go from a few to tens of milliseconds and that is caused by the link and the physical layers [8]. This delay could affect the operation of several CC schemes that rely on the measurements of end-to-end delays and Round Trip Times (RTT) to detect congestion and make decisions.
• Time-varying links: another peculiar characteristic of wireless links is that they are time-varying [2, 9, 10, 11], meaning that their capacity exhibits multiple shifts over time that are hard to predict. Indeed, it has been shown that there is no guarantee of link bitrate in wireless and cellular networks (e.g., LTE channels [9]) as they are easily affected by the user's motion and orientation. These shifts in capacity could be far too fast for end-to-end congestion control to evaluate the status of the network and react in a timely manner. As a result, traditional CC schemes are not efficient when it comes to wireless networks, suggesting the need for more reactive CC schemes that potentially consider information from the wireless media to better deal with time variability.
• Mobility and handover impact: mobility and handover are fundamental concepts in wireless networks. However, they may result not only in packet loss but also in bufferbloat (i.e., excessive buffering) and burst of packets. For instance, during a handover, packets might be kept in the queue before being transmitted in a bursty manner once connected to the new cell. Existing CC schemes may over-react to the incurred bufferbloat as they could not distinguish between the delay caused by a normal congestion and the one caused by a handover [3]. As a result, the end-to-end throughput and delay could be severely impacted [12] and the impact may be exacerbated with the mobility and the ping-pong effect (i.e., when a user goes back and forth between two cells).
• Variable bitrate of XR and real-time media traffic: existing studies have established that the output bitrate from media (video/audio) encoders has rarely a constant bitrate and that there could be also bitrate bursts and high levels of variations over time depending on the codec and the end-user behavior [9]. For instance, a static content in a video might give zero bitrate, during which the flow could lose its share of bandwidth in the network. When the encoder resumes generating data, sudden shifts or bursts in the bitrate could occur, affecting the size of the queues, the delays and the jitter [13]. The congestion control scheme should be aware of such behavior and be able to continuously ensure the required bandwidth while avoiding packet bursts,  bufferbloats and jitter.
• Performance requirements of XR applications: XR interactive applications have tighter requirements in terms of packet loss, delay and bandwidth than traditional applications and are more sensitive to the variation of such metrics. For instance, in real-time video streaming, packet loss could result in distorted video images while large delay variations could lead to a choppy video [9]. For real-time and interactive XR applications, performance requirements are stringent (e.g., 1 to 10ms delay and high bitrates) and could be variable over time, even for the same flow, depending on the application’s context [15]. In this case, congestion could affect the application’s interactivity and introduce delays and packet loss, affecting the users’ quality of experience. As a result, congestion control needs to be aware of such requirements and should be performed in coordination with the application [5].

Key Research Directions

• Wireless-aware congestion control: as the performance of wireless networks is affected by mobility and interference leading to an abrupt capacity change or variable and unpredictable loss and delays over time, an appealing research avenue would be to study the resilience of existing CC schemes and their responsiveness to sudden changes in network conditions [10, 14]. Indeed, the reaction of existing CC schemes are in the order of one or multiple RTTs [13], and hence, they are not fast enough to cope with the time variability incurred by wireless links and the XR applications. It is therefore of utmost importance to devise novel wireless-aware congestion control schemes that are able to adapt in real-time to time-variability and take into consideration non-congestive loss and delay. For instance, the CC scheme should ideally leverage information provided by the wireless routers (e.g., link status, channel quality, handovers) and by the XR application (e.g., requirements, codec rate).
• XR and real-time Media transmission-aware Congestion Control: ideally, congestion control should  operate in coordination with the media encoder (e.g., video or audio). For instance, the CC scheme could request the codec to adjust its rate based on the status of the network and the available bandwidth. Similarly, the codec could provide input to the congestion control like the bandwidth and delay requirements, the priority of packets, and their content (e.g., video frames that they carry). As such, when multiple media streams are transmitted simultaneously, congestion control could consider their different requirements and priorities, the type of media carried by each flow and then could adjust the sending rate accordingly.  Recent efforts have also started looking at the possibility of incorporating some metadata into the packets with information about the transported media and the performance requirements to allow the congestion control scheme and the buffering scheme to better manage the available bandwidth [10].
• Traffic Control: traffic shaping, packet scheduling, prioritization, classification, active queue management and policing are the key traffic control functions that are implemented in the network in order to achieve several objectives like reducing congestion, maximizing throughput,  and providing better control of delays and bufferbloats. These functions have strong interplay with the congestion control running at the end hosts as they both control packets and the overall network performance. In this context, a key challenge is to study the interaction between the CC scheme running on the endpoints and the traffic control schemes running on the core routers and understand how their respective decisions affect each other and impact the performance. The second challenge is to design traffic management schemes combining the aforementioned functionalities while considering the characteristics of the CC schemes implemented at the endpoints. Indeed, ideally, traffic control functions in the network and the congestion control scheme at the endpoint should be aware of each other and should coordinate to efficiently achieve the sought-after performance objectives.
• Coordinated Congestion Control: in current deployments, transport flows running on the same host are handled by different instances of CC schemes that are operating independently from each other. However, ideally, flows that are experiencing the same congestion behavior and have common characteristics (e.g., same source/destination, same bottleneck link or forwarding path) should be designated as a “group of flows” and their congestion control should be coordinated in order to ensure a more efficient sharing of the available bandwidth.

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