Telecommunications Science ›› 2021, Vol. 37 ›› Issue (1): 8-21.doi: 10.11959/j.issn.1000-0801.2021020
• Comprehensive Review • Previous Articles Next Articles
Shihang ZHANG, Yawen LUO, Min ZHANG, Qifu SUN, Xiaolong YANG
Revised:
2020-11-25
Online:
2021-01-20
Published:
2021-01-01
CLC Number:
Shihang ZHANG, Yawen LUO, Min ZHANG, Qifu SUN, Xiaolong YANG. Research progress of mobile video prefetching for mobile content distribution networks[J]. Telecommunications Science, 2021, 37(1): 8-21.
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参考文献 | 方案 | 优点 | 缺点 |
参考文献[ | 综合用户偏好和人群移动预测流行度 | 命中率高 | 难以满足个性化需求 |
参考文献[ | 基于深度学习的两阶段预取 | 准确性高 | 计算成本较高 |
参考文献[ | 时间序列预测模型 | 在上下文敏感的内容上表现优异 | 只适用于时间高度相关的内容 |
参考文献[ | 张量学习 | 预取命中率和准确率优于传统方案 | 计算成本较高 |
参考文献[ | 基于k-means算法的非监督式学习机制 | 预取准确性提高且可扩展性强 | 计算成本较高 |
参考文献[ | 多层感知机预测局部流行度 | 合成的静态数据表现良好 | 数据时变性较强的情况下表现较差 |
参考文献[ | 根据用户宣布决定预取 | 预取内容更加明确 | 不适用于随机观看情形 |
参考文献[ | 插入书签和预取状态 | 引导用户观看 | 只适用于局部预取 |
参考文献[ | 协同过滤算法 | 内容特征较普遍时表现优异 | 难以预测个性化内容 |
参考文献[ | 基于深度强化学习的多视点视频主动缓存 | 视图增加时系统性能保持稳定 | 未考虑有限带宽资源 |
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参考文献 | 方案 | 优点 | 缺点 |
参考文献[ | 固定段预取 | 实现简单 | 易受网络波动影响 |
参考文献[ | 自适应段预取 | 减轻网络波动影响 | 未经验证 |
参考文献[ | 上下文感知 | 动态调整预取策略 | 浪费网络资源 |
参考文献[ | 级联神经网络模型 | 热点块的平均比特率提高了14.31% | 模型设计复杂 |
参考文献[ | 深度神经网络模型 | 准确率较高 | 模型设计复杂 |
参考文献[ | 非高峰时期预取 | 有效缓解高峰时期的带宽压力 | 预取时效性差 |
参考文献[ | 动态规划算法 | 提高存储单元使用率 | 计算空间需求高 |
参考文献[ | 长短期混合时间内容更新方案 | 提高空间利用率且明显降低功耗 | 计算方法复杂 |
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