面向移动内容分发网络环境的移动视频预取技术研究进展

doi:10.11959/j.issn.1000-0801.2021020

Abstract

Abstract:

With the development of mobile internet and the popularization of video applications, the mobile content distribution network (MCDN) emerged as the times require.Prefetching technology is one of the key technologies for content distribution.It predicts the user’s future requests and prefetches the predicted content to the network edge in advance, thereby reducing the data acquisition delay and improving the quality of experience (QoE).In order to elaborate on the development status of mobile video prefetching technology, the latest research results of mobile video prefetching technology were introduced from the perspectives of user’s mobile behavior perception, content attribute perception, and network resource perception.And finally its main advantages and disadvantages were analyzed and the direction of further research in the future was pointed out.

Key words: mobile content distribution network, video prefetching, mobility of users, quality of experience

CLC Number:

TN91

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.

Figures/Tables 6

References 63

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参考文献	方案	优点	缺点
参考文献[15]	建立个体移动模型	减轻网络拥塞，降低服务成本	建模困难
参考文献[17]	建立群体移动模型	显著提高预取命中率且更易建模	忽略个性化需求
参考文献[23]	LSTM预测位置	预测精度高，召回率可达100%	时间相关度要求高
参考文献[28]	k-means算法划分用户组	预取准确性提高，平均预测精度为84.5%	未涉及用户隐私保护
参考文献[29]	基于社交和内容感知的预测	提前预测视频是否会被预取	计算量较大

参考文献	方案	优点	缺点
参考文献[17]	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[23]	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[35]	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[36]	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[37]	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[38]	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[39]	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[40]	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[44]	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[45]	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

参考文献	方案	优点	缺点
参考文献[48-49]	固定段预取	实现简单	易受网络波动影响
参考文献[50]	自适应段预取	减轻网络波动影响	未经验证
参考文献[51]	上下文感知	动态调整预取策略	浪费网络资源
参考文献[52]	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[53]	深度神经网络模型	准确率较高	模型设计复杂
参考文献[54]	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[56]	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[59]	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

Research progress of mobile video prefetching for mobile content distribution networks

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