With the wide application of emerging technologies such as ultra-high definition video in Vehicular Edge Computing (VEC), the massive heterogeneous video data generated by vehicles have put forward higher requirements for real-time performance, energy efficiency and accuracy of processing. However, higher video analysis accuracy often leads to an increase in delay and energy consumption. How to balance the relationship between the three is an urgent problem to be solved. Meanwhile, the balanced or fixed bandwidth allocation mechanism adopted by most studies often ignores the characteristic differences of video tasks, resulting in inefficient resource allocation. At the same time, the security risks in the Internet of vehicles cannot be ignored. In order to deal with these challenges, this paper proposed a distributed task offloading framework combining Analytic Hierarchy Process (AHP) and Deep Deterministic Policy Gradient (DDPG). An adaptive bandwidth allocation mechanism based on the characteristics of video tasks is designed, and an improved blockchain consensus mechanism is introduced to ensure the optimal offloading decision in a trusted environment. Experimental results show that compared with the existing offloading schemes, the proposed algorithm reduces the task offloading delay by about 7.54%, reduces the energy consumption by about 6.37%, and improves the accuracy of video analysis by about 5.02% while ensuring security.

Data read and write tail latency in distributed storage systems affects the quality of service of applications. In this paper, we focus on requests with latency around 99.99th tail latency and design a critical window. By analyzing the target storage device distribution of requests in the critical window, we design a simple but effective cache space allocation method to optimize tail latency. Unlike traditional methods, it schedules target cache space allocation instead of requests. Since it does not change the processing of requests and I/Os, it reduces the extra time consumption incurred by the scheduling algorithm. At the same time, it solves the problems of lag, tail latency fluctuation, and high resource consumption of the load balancing-based tail latency guarantee algorithm on request scheduling. Finally, we verify the optimization effect of the method’s tail-latency metrics.

Despite the prevalence of Crowdsourced Live Video Streaming (CLVS), video viewers still suffer from low QoE particularly under rush hours, as the existing Content Delivery Network (CDN) is not scalable enough to handle the massive concurrent streaming. The rapid emergence of Web 3.0 provides new incentives for revisiting and applying the classical P2P networking in CLVS. However, the highly dynamic joining or leaving behavior of CLVS viewers frequently interrupts the real-time streaming and leads to low QoE, which demands to retrofit P2P. In this work, we bridge the gap by proposing a reliable P2P-assisted CLVS system named Venus, where viewers can share their streaming content smoothly, without video freeze regardless of viewers leaving. To realize Venus, different from the single-flow sharing in previous P2P video streaming, we design a novel multiflow framework with lightweight redundancy encoding, so as to handle the inherently high viewer dynamics. Correspondingly, we introduce a multiflow scheduler to enable QoE adaption concertedly over heterogeneous multiple flows. Real-world evaluation confirms the benefits of decentralized CLVS streaming, with Venus outperforming the state-of-the-art CDN solution by almost totally eliminating the video stall while enhancing the video quality by 10.2%.