Clock-free scheduling reduces sync delays in real-time content delivery
Clock-Free Stochastic Scheduling for Real-Time Synchronized Content Delivery
Networking and Internet Architecture
Summary
In online activities like cloud gaming and live streaming, it’s important for all users to see the same thing at the same time. The authors found a way to make this happen without relying on synchronized clocks or complicated client data. Their system adjusts delivery times using simple feedback and smart math to keep users’ experiences closely matched, even if network delays vary. Tests show this method delivers better synchronization than previous approaches.
What this means in practice
- •For cloud gaming platforms: Coordinate server delivery times to keep players’ game views perfectly synced without extra client synchronization data.$Commercial implications: Enables more reliable, latency-minimized multiplayer gaming experiences, improving service quality and player satisfaction.
- •For video streaming services: Manage content delivery to a group of viewers to reduce uneven delays and improve live event synchronization.
Authors
Xuan Wang, Xiaoyi Wu, Ying Sun, Atilla Eryilmaz, Bin Li
Abstract
Real-time synchronized content delivery is critical for applications such as cloud gaming, collaborative mixed reality, and live streaming, where a server must ensure that a group of users perceives the same content simultaneously for each delivery. Due to heterogeneous and unpredictable network delays, ensuring synchronization is highly challenging. We propose a clock-free scheduling framework that introduces latency compensation to control the server's delivery time for each user, aiming to maximize synchronization performance (i.e., minimizing the gap between the maximum and minimum latencies) while maintaining a low overall latency cost. Unlike prior work requiring synchronized clocks or client-side timestamps, our approach uses only acknowledgment feedback, enabling flexible deployment across diverse environments. We formulate the problem as a convex stochastic optimization with a non-smooth objective. To solve the problem, we employ the stochastic proximal point (SPP) algorithm that updates the delay compensation, using only the previous latency samples, rather than the latency distribution. By exploiting the subproblem structure, we apply ADMM to enable efficient updates with per-iteration complexity of at most $\mathcal{O}(N\log N)$, where $N$ is the number of users. We establish a convergence rate of $\mathcal{O}(1/\sqrt{K})$, where $K$ is the number of content deliveries for transmission. The framework is further extended to handle transmission failures, where packet loss results in successful delivery to only a subset of users. Finally, simulations using both synthetic and real-world latency traces demonstrate the consistent superiority of our algorithm against baseline methods.