## 3 Simulation Results and Discussion
Figure 8: Bandwidth allocation for on-demand streaming and 360-degree streaming prior to the algorithm’s implementation. Figure 9: Bandwidth allocation for videotelephony and live streaming applications prior to the algorithm’s implementation. In scenarios where the bandwidth requirements of streaming applications for autonomous vehicle passengers cannot be fully met, the proposed \"Streaming Service Support\" module comes into play. This module dynamically adjusts the bandwidth requirements and quality of the streaming service to address disparities between the supply and demand for bandwidth, particularly during peak usage times. Additionally, the \"Real-time Streaming Bandwidth Allocation\" module prioritizes the redistribution of the initially allocated bandwidth, originally designated for non-real-time applications, to support real-time videotelephony and live streaming demands. Alternatively, through V2V communication, the bandwidth supplied by base stations located on other road segments can be relayed to facilitate seamless videotelephony and live streaming for users. Specifically, after im  
Figure 11: Bandwidth allocated for applications of videotelephony and live streaming after applying the proposed algorithm. Figure 10: Bandwidth allocated for applications of on-demand streaming and 360-degree streaming after applying the proposed algorithm. plementing the proposed algorithm, the bandwidth for real-time videotelephony and live streaming applications improves by 73% and 60%, respectively, during the high-demand periods from 7:00 a.m. to 10:00 p.m. Regarding on-demand streaming and 360-degree streaming, the \"Video Clip Preloading\" module is employed to preload video clips for autonomous vehicle passengers before their playback times. In cases where these vehicles converge on the same road segment as the supported autonomous vehicles facing bandwidth limitations for on-demand and 360-degree streaming, video clips can be preloaded to the conveying vehicles within the coverage of base stations with sufficient bandwidth. The preloaded video clips can then be transmitted to the requesting autonomous vehicle passengers for playback through V2V communication. Consequently, bandwidth availability for on-demand and 360-degree streaming sees improvements, with increases of 58% and 57%, respectively, during the high-demand periods from 7:00 a.m. to 10:00 p.m. This improvement underscores the effectiveness of this work in prioritizing real-time applications and optimizing non-real-time streaming applications through the proposed preloading strategies. Figure 12 presents a comparison between the bandwidth requested by various streaming applications and the actual bandwidth allocated before and after the implementation of the proposed algorithm. Between 11:00 p.m. and 6:00 a.m., there is relatively low demand for bandwidth from autonomous vehicle passengers, and almost all applications successfully receive sufficient bandwidth allocation. However, from 7:00 a.m. until 10:00 p.m., prior to the algorithm's implementation, numerous applications have already reached the maximum bandwidth limit set by the congested road segment's base stations. Consequently, their bandwidth requirements remain unfulfilled. Moreover, as the number of applications increases, the disparity between the obtained bandwidth and the demanded bandwidth becomes more pronounced. During the period from 7:00 a.m. to 10:00 p.m., when the base stations on congested road segments had reached their maximum bandwidth capacity, all passengers in autonomous vehicles experienced an average bandwidth improvement of 55%. Furthermore, throughout the two peak demand intervals, in the morning at 8:00 a.m. and in the evening at 7:00 p.m., passengers in autonomous vehicles saw remarkable increases of 90% and 100%, respectively, in the available bandwidth. Figure 4:0 User-required bandwidth and the bandwidth available before and after applying the algorithm.