Many applications, such as AI training and distributed storage, rely on cross-datacenter (DC) networks to provide services. For compatibility with existing RDMA hardware and to improve quality of service, network providers connect to datacenters over dedicated lines. However, the current RDMA congestion control has some problems in cross-DC environment. First, due to the large bandwidth delay product (BDP) of cross-DC flows, the switch frequently triggers PFC, which impairs the transmission of all flows. Meanwhile, due to the lag of congestion signals, congestion control algorithms may cause unfair bandwidth allocation between intra-DC flows and cross-DC flows. In addition, cross-DC traffic will experience severe queuing at the data center interconnect (DCI) switch, increasing queuing delay. To address these challenges, this paper proposes MLCC, a cross-datacenter congestion control algorithm based on the fast control loop. MLCC uses micro congestion control loops to achieve fine-grained network state awareness and accurate rate adaptation, and reduce queue length in the transmission path. Experimental results show that MLCC can quickly converge all flows to fairness, achieve high link utilization, and ensure low queue length on the switch. Large-scale simulations show that MLCC can reduce the average FCT of intra-datacenter and cross-datacenter traffic by up to 46% and 27%, respectively.

Real-time video (RTV) systems place high demands on ultra low-latency (i.e., less than 100 ms). However, our large-scale measurements reveal that a significant portion of users still experience high video frame latency due to bandwidth jitters. Existing solutions attempt to mitigate this issue by lowering the sender's future video frame encoding bitrate. Nevertheless, as shown in our controlled experiments, they fail to drain existing packets queued on the bottleneck node (i.e., the 5G base station and Wi-Fi access point), still suffering from high tail latency as bandwidth decreases. In this paper, we propose Co-RTV, a collaborative RTV system that achieves predictable latency control. Specifically, Co-RTV enables endpoint-network collaboration between the bottleneck node and the sender. The collaboration speeds up the release of packets queued at the bottleneck node and facilitates accurate latency control at the RTV sender through scalable QoE-driven flow control. Extensive experiments in emulated networks and on a 5G testbed demonstrate the superior performance of Co-RTV, with tail latency reductions of 69.1% and 70.5%, respectively.

Reliable low-latency media streaming is increasingly critical for delivering seamless interactive and immersive services. To meet this need, the IETF and 3GPP have introduced the Low Latency, Low Loss, Scalable Throughput (L4S) architecture, which mitigates queuing delays in IP traffic and supports latency-sensitive applications. This paper focuses on real-time video streaming and proposes an enhanced framework, Enhanced L4S (EL4S), which integrates a link load factor feedback mechanism to more accurately reflect real-time network conditions. We design and implement a cross-layer end-side transmission control algorithm based on EL4S to improve real-time video performance over 5G networks. In this design, EL4S encodes link load information into packets at the 5G core and uses ACK-based feedback to inform the sender about current network states. At the sender, a dynamic bitrate adaptation algorithm adjusts the transmission rate in response to the reported link load factor, balancing throughput and delay while avoiding network congestion. This adaptive mechanism enables precise, frame-level control over video encoding rates and transmission behavior. Extensive experimental evaluations demonstrate that EL4S achieves high link utilization and low latency, significantly outperforming existing baseline algorithms. Overall, EL4S provides an efficient and deployable solution for achieving low-latency, high-quality real-time streaming in dynamic 5G networks.

Access networks, even with advanced 5G technology, often face bottlenecks when supporting concurrent real-time Ultra High Definition (UHD) video streams with high bandwidth and low latency (e.g., under 10 ms of one-way delay) requirements. Traditionally, end systems employ a combination of flow and congestion control mechanisms to control the sending rate to avoid overwhelming the receiver and the network. However, such control efforts induce prolonged tail delays, thereby sharply reducing the number of UHD video streams meeting delivery deadlines, and sometimes even zero. These outcomes are largely due to the inaccurate network status estimation associated with the control mechanisms. To address this challenge, we propose CFC, a deadline-oriented flow control mechanism that employs cross-layer status estimation to maximize user satisfaction with deadlines. CFC accurately assesses cross-layer information, including flow status and 5G access network status at minimal expense, thus ensuring the deadlines through effective concurrent flow control. Our experiments, conducted in both simulation and testbed settings, demonstrate significant improvements in delay and load-balancing for both reliable and unreliable transmissions.

Cloud virtual reality (VR) has emerged as a promising technology, offering users a highly immersive and easily accessible experience. However, concurrent pulse VR flows can lead to significant congestion in 5G base stations, making network providers unable to guarantee the delay requirements for all users. Based on a comprehensive analysis of the poor delay per-formance of cloudVR flows within the existing 5G edge network, we propose a novel cross-layer congestion control mechanism that is assisted by access network status and flow characteristics. This mechanism is deployed within the 5G edge network and enables efficient global scheduling of concurrent flows. Experiment results show that our mechanism greatly optimizes the network delay in concurrent scenarios and guarantees the delay requirements of all users while avoiding network overload. Our work underscores the advantage of leveraging 5G edge nodes as a valuable resource to meet the anticipated demands of future services effectively.

VR applications that require extremely low latency and high image quality are widely used in online games and other 5G scenarios, becoming a key research field in recent years. However, the limited bandwidth in 5G edge networks fails to meet the peak rate requirements for multiple VR flows. MPTCP is suitable for 5G edge networks, supporting the simultaneous use of multiple networks on mobile devices. Nevertheless, accurately scheduling VR data blocks to different sub flows to satisfy their low latency requirements is challenging due to their micro-burst characteristic. In this paper, we propose a novel MPTCP scheduler for cloud VR applications in 5G edge networks, called the Cross-Layer Information-based One-Way Delay Predictive Scheduler (CPS). CPS accurately predicts one-way delay by incorporating cross-layer information from both the application and edge wireless sides, and adaptively schedules VR data blocks to the optimal subflow. Experimental results show that CPS outperforms existing strategies, supporting 125% more users for VR applications in the typical scenario. Additionally, CPS maintains completion times for 99% of cloud VR packets below 7 ms. CPS successfully meets the quality of experience needs of more users, providing a promising solution for large-scale deployment of cloud VR services in 5G edge networks.

The pulse pattern generated by real-time cloud VR applications poses a new challenge to bandwidth-limited wireless access networks. Concurrent pulse flows can lead to significant congestion in access points, making network providers unable to guarantee the quality of experience (QoE) for all users. To maximize the number of satisfied users, we propose a cross-layer assisted traffic scheduling mechanism, CTS, to manage flows into the edge network. We evaluated CTS using a trace-driven simulator, and the experimental results prove that CTS achieves the theoretical maximum of satisfied users and maintains a more balanced load for the network.

The IPv6 Segment Routing (SRv6) is a promising solution to support services such as service function chain (SFC) and network function virtualization (NFV). But the SRv6 implementation in the Linux kernel is being criticized for lack of programmability and scalability. In this paper, we present an efficient implementation of the SRv6 data plane based on AF_XDP (SRA) in userspace. By leveraging the AF_XDP supported in the Linux kernel, we implement a high-performance and programmable framework that allows network operators to encode their own network functions. Moreover, these functions can automatically execute in userspace and Linux network namespaces while processing specific packets. In addition, SRA also implements SR-proxy to support the Virtual Network Functions (VNFs) chaining based on SRv6. Experimental results show that SRA achieves high performance and enhances integration with the kernel ecosystem. In all scenarios, SRA processes faster than other implementations, such as the SRv6 implementation in the Linux kernel and SREXT module, and in some scenarios, SRA is even 10 times faster than the SRv6 implementation in the Linux kernel. Meanwhile, the proposed architecture can be easily extended to support new SRv6 behaviors and network functions.

The millimeter wave technology which provides the throughput of multi-gigabit per second is one of the key technologies for 5G/B5G communications. However, an optimal interaction between the transport layer protocols and the highly fluctuating millimeter wave networks is extremely challenging and lacks in-depth exploration in actual networks. In this paper, we examine and discuss the performance of several TCP congestion control algorithms in the real 60 GHz millimeter wave environment, and inspect the improvement of TCP performance over mmWave hybrid networks by TCP proxies in single-flow and multi-flows scenarios. Our results reveal severe adaptation problems associated with these congestion control algorithms over millimeter wave networks and the effectiveness of TCP proxies for the utilization of millimeter wave hybrid networks.