目的 基于缓存的自适应视频流传输策略无需估测实时带宽，直接通过缓存变化量与码率的映射函数选取符合当前网络状况的最佳质量码流传输。传统基于缓存的自适应视频传输不考虑内容特征，在码率选择上为不同运动级别视频内容均使用相同的码率映射函数，在不稳定的无线网络环境中高运动强度内容的码率急剧降低会严重伤害用户体验质量（QoE），提出运动感知基于缓存的自适应视频流传输（MA-BBA）算法。方法 MA-BBA算法根据片段运动级别确定码率映射函数，对运动强度高的内容快速切换到较高码率，而对于运动强度较低的内容则使用较为保守的码率，从而使得缓存资源能够位于安全边界之上且较多分配给高级别运动内容，提高不同运动强度内容的平均质量，使整体QoE得到优化。结果 在公开的无线网络带宽数据集上实现本文MA-BBA算法，基于吞吐量的自适应传输算法（TBA）和基于缓存的自适应传输算法（BBA）。MA-BBA在高运动强度内容的平均质量上比TBA和BBA分别提高1.7%和1.2%，且质量波动区间更小。MA-BBA在平均缓存利用率上达到72%，大大高于TBA的45.9%和BBA的45.4%。结论 MA-BBA算法与现有的码率自适应算法TBA和BBA相比，大大提高了缓存资源利用率，提高了对资源要求最苛刻的高级别运动内容的传输质量，减小码率切换幅度频率，优化了视频服务的整体QoE。

Objective Nowadays, dynamic adaptive streaming over HTTP (DASH) has been widely adopted for providing continuous video delivery service under various network conditions and heterogeneous devices, and bitrate adaptation algorithm is the most important feature of the DASH service. State-of-the-art bitrate adaptation algorithms are classified as two types:throughput-based methods and buffer-based methods. The throughput-based adaptive video streaming often estimates the current bandwidth on the smoothed throughputs collected in a time window and chooses the best suitable presentation for streaming to the client. While the buffer-based adaptive video streaming needs not to estimate the real-time bandwidth, and directly selects the best quality representation according to the current network status through the mapping function from the buffer occupancy to the bit-rate. However, this conventional buffer-based adaptation algorithm without considering the content features based rate selection for different motion video content, sudden rate fluctuations on high motion content would severally harm the user quality of experience (QoE) in an unstable wireless network. A motion-aware buffer-based adaption (MA-BBA) is proposed to determine the bit-rate mapping function based on motion rank from buffer occupancy for each segment. Method Commonly, high motion content in a streaming means the most import part to attract viewer's interest, and which should be streamed in a high quality version to obtain high QoE, while the high motion content needs more resource than slow motion content at the same quality. To reduce the quality fluctuations and assure better average quality best effort, the bit rate of high motion content should be mapped to higher bit-rate version than the slow motion content according to the current bandwidth. The MA-BBA assumes different bit-rate mapping policies for different motion content. It maps higher bit-rate for high motion segment, and while maps the more conservative bit-rate for those slower motion segments, which also results increasing the buffer resources to prevent rebuffering. Even though the mapped bit-rate for high motion segment exceeds current bandwidth sometimes, MA-BBA would consume a certain proportion of available pre-buffering occupancy above the safe boundary to assure high motion content streaming on higher bitrate than current bandwidth. Result We have implemented three adaptation algorithms including the proposed MA-BBA, throughput-based adaptation (TBA) and buffer-based adaptation (BBA) on a set of public online wireless traces, among which the QoE metrics and network performance have been evaluated. Compared with the conventional TBA and BBA, the proposed MA-BBA performs better average quality on high motion content respectively, which has been proved 1.7% higher than TBA and 1.2% higher than BBA, and MA-BBA also results less quality fluctuations than the other algorithms. Furthermore, the average utilization rate of buffer occupancy in MA-BBA has been reported up to 72%, which is greatly higher than 45.9% of TBA and 45.4% of BBA. Conclusion Comparing with TBA and BBA, MA-BBA improves the utilization of buffer resource, and improves average streaming quality of high motion content in constrained resource environment. MA-BBA also reduces the amplitude and frequency of bit-rate switching, and then the overall QoE of video service has been improved. MA-BBA have implied future direction on adaptive streaming that important content as well as semantic content should be optimized to better QoE than those ordinary content, thus a novel solution on content adaptation could be introduced to the emerging wireless applications such as smart helmet, unmanned aerial vehicle, remote medical technology in resource limited environment.