The proliferation of virtual reality (VR) content, particularly in resource-intensive applications, has been met by remote rendering to overcome local hardware limitations. Along with numerous advantages, remote rendering VR brings about a new traffic type that features huge throughput and burstiness generally, the understanding and modeling of which is critical for performing VR networking optimization to guarantee the Quality of Experience (QoE) of VR traffic transmission, including synthetic traffic generation and Network Slicing orchestrators. However, existing VR traffic modeling studies are limited in that they do not consider the impact of user interactions on VR traffic. In contrast, we carry out extensive traffic measurements in this paper, and discover that head movements actively affect the VR frame sizes generated. We analyze traffic features and further model the relationship between angular velocities and frame sizes quantitatively. A linear regressor is modeled to predict the frame size by considering history frame sizes and angular velocities jointly. We use Air Light VR (ALVR) to stream VR content in various scenarios, construct the datasets, and validate our model on top of them. The result shows that the our model is capable of reducing the 95% square prediction error by 18-30 compared to the state-of-the-art model. To the best of our knowledge, this is the first investigation into the intricate relationship between remote rendering VR traffic and head movement. Our dataset and results will be publicly available and reproducible.

The rapid growth of online interactive video applications reflects the increasing popularity of real-time communication (RTC). Despite advancements in network and video technologies, the worse quality of experience (QoE) such as large delay, rebuffering and low image quality, etc. remains to be complained generally. We argue that this is mainly due to the legacy network-oriented congestion control (CC), which assumes continuous stream of packets are sent. But it is not satisfied for RTC since bit-rate variation is inherent to RTC’s video encoder.

Interactive video applications signal widespread interest in Real-time communication (RTC), yet issues like frame delay, rebuffering, etc. remain a common complaint. We argue that this is mainly due to legacy network-oriented congestion control (CC), which assumes a continuous stream of packets is being sent. But this assumption doesn't hold in RTC since the video encoder exhibits inherent bit-rate variation: (1) Bursty spiking bit-rate leads to packets waiting in the sending buffer, which increases frame delay. (2) Low bit-rate causes insufficient packets available for sending, making current CCs hard to detect available bandwidth. In response, we propose BurstRTC, a novel paradigm for RTC transport protocol. Each frame is emitted as a whole, and the video bit-rate is directly controlled by network congestion feedback. BurstRTC uses frame-bursting to estimate available bandwidth efficiently regardless of bit-rate variation. Considering the impact of bit-rate variation on network congestion, BurstRTC models frame size as a Gaussian distribution instead of a fixed size and further derives its frame delay, preventing suboptimal performance of purely network-oriented designs. An analytic method for determining the target bit-rate replaces the trial-and-error updates of gradient-based methods, ensuring fast convergence to the available bandwidth. We evaluated the performance of BurstRTC and found that, compared with GCC, BurstRTC achieves up to$59.8 \%$higher bit-rate and up to$\mathbf{4 8. 9 \%}$lower frame delay. Further, compared with SQP and Pudica, BurstRTC can also reduce tail frame delay by up to$89.2 \%$, and improve average bit-rate by up to$15.6 \%$.

The advancement of next-generation networks such as 5G/6G and satellite systems has significantly increased available network bandwidth, while also exacerbating network burstiness. This surge presents a formidable challenge for congestion control (CC), a pivotal mechanism for achieving high bandwidth utilization and low latency by adjusting congestion windows or modifying sending rates. Traditional end-to-end CC algorithms fall short of optimality due to their reliance on congestion signals in acknowledgment packets, which introduce a delay of one round-trip time (RTT). In this paper, to mitigate end-to-end delayed feedback, we introduce a novel Real-Time Congestion Control (RTCC) algorithm that integrates machine learning with conventional model-based CC. RTCC employs a Multi-Layer Perceptron (MLP) to model network conditions and predict current congestion signals accurately. A tailored network model then utilizes these predictions to manage packet accumulation in the network bottleneck. Moreover, an online model-updating mechanism is proposed to adapt to diverse network environments. We integrate RTCC into QUIC and conduct comprehensive experiments in both emulated test-beds and real-world settings, including WiFi/4G/5G and cross-continent networks. The results demonstrate RTCC's efficacy, with up to a 32% increase in average throughput, a reduction in RTT by up to 21% compared to BBR V2, and the maintenance of fair bandwidth allocation.