## 1 Introduction  
Distributed video streaming systems have undergone a significant technological evolution over the past decade, shifting from centralized to increasingly decentralized paradigms [1]. Traditional streaming architectures face considerable technical bottlenecksin scenarios involving large-scale concurrency and geographic distribution [2]. The fundamental limitations of centralized architectures stem from their inherent single point of failure risks and linear scaling costs, as server bandwidth and computational resource demands escalate exponentially with user growth [3; 4]. As the scale of streaming applications continues to expand, the traditional centralized server-centric content-distribution model has revealed clear scalability bottlenecks [5]. Centralized architectures typically suffer from soaring server loads, network-bandwidth saturation, and single points of failure during high-concurrent access [6]. Concurrently, the upload bandwidth and computing resources of edge nodes often remain underutilized. Peer-to-peer (P2P) network architectures address the inherent scalability issues of centralized systems by enabling each node in the network to act as both a consumer and a distributor of content, thus achieving a dynamic balance between resource supply and demand [2]. In P2P streaming systems, nodes can directly establish data transmission links, forming distributed content distribution networks, such as chain or mesh topologies. This effectively offloads the central server, enhancing system resilience and fault tolerance [7]. However, large-scale P2P deployments introduce their own set of technical challenges, including topology instability due to dynamic node behavior (frequent online/offline transitions), maintaining content data consistency, multi-source synchronization control, and mechanisms for identifying and protecting against malicious nodes [8]. These factors impose stringent requirements on system real-time performance and robustness. However, existing P2P streaming solutions, while alleviating the load on central servers, still face several critical limitations. First, topological instability is a persistent challenge: frequent node churn (nodes joining and leaving) leads to dynamic changes in the overlay network, which can cause link disruptions and degrade service continuity. Second, synchronization across heterogeneous networks and devices is difficult to guarantee, resulting in inconsistent playback quality and increased latency, especially in multi-source or multi-hop scenarios. Third, many P2P systems lack robust mechanisms for rapid fault detection and recovery, making them vulnerable to cascading failures. Moreover, security and trust management remain open issues, as malicious or low-quality nodes can disrupt the overall service. Additionally, the scarcity of IPv4 addresses has necessitated widespread deployment of Network Address Translation (NAT) technologies, which significantly complicate P2P implementations. NAT creates complex network topologies where direct peer-to-peer connections become challenging or impossible, often requiring sophisticated traversal techniques such as STUN, TURN, or UPnP that introduce additional performance overhead and connection establishment delays [9; 10]. These NAT-related complexities not only increase system deployment difficulty but also reduce connection reliability and introduce unpredictable latency variations that can severely impact real-time streaming performance. These limitations highlight the need for more resilient, adaptive, and self-healing P2P architectures. In recent years, real-time communication scenarios such as remote conferencing, online education, and interactive live streaming have posed increasingly stringent requirements on video transmission, especially regarding latency, jitter, and data integrity [11].