Hypertext transfer protocol(HTTP) adaptive streaming(HAS) plays a key role in mobile video transmission. Considering the multi-segment and multi-rate features of HAS, this paper proposes a buffer-driven resource manag...Hypertext transfer protocol(HTTP) adaptive streaming(HAS) plays a key role in mobile video transmission. Considering the multi-segment and multi-rate features of HAS, this paper proposes a buffer-driven resource management(BDRM) method to enhance HAS quality of experience(QoE) in mobile network. Different from the traditional methods only focusing on base station side without considering the buffer, the proposed method takes both station and client sides into account and end user's buffer plays as the drive of whole schedule process. The proposed HAS QoE influencing factors are composed of initial delay, rebuffering and quality level. The BDRM method decomposes the HAS QoE maximization problem into client and base station sides separately to solve it in multicell and multi-user video playing scene in mobile network. In client side, the decision is made based on buffer probe and rate request algorithm by each user separately. It guarantees the less rebuffering events and decides which HAS segment rate to fetch. While, in the base station side, the schedule of wireless resource is made to maximize the quality level of all access clients and decides the final rate pulled from HAS server. The drive of buffer and twice rate request schemes make BDRMtake full advantage of HAS's multi-segment and multi-rate features. As to the simulation results, compared with proportional fair(PF), Max C/I and traditional HAS schedule(THS) methods, the proposed BDRM method decreases rebuffering percent to 1.96% from 11.1% with PF and from 7.01% with THS and increases the mean MOS of all users to 3.94 from 3.42 with PF method and from 2.15 with Max C/I method. It also guarantees a high fairness with 0.98 from the view of objective and subjective assessment metrics.展开更多
基金Supported by The National Natural Science Foundation of China(10771171)555 Innovation Talent Project of Gansu Province(GS-555-CXRC)+1 种基金Technique Innovation Project of Northwest Normal University(NWNU-KJCXGC-212)Important Foundation of Dingxi Teachers College(TD2016ZD06)
基金supported by the 863 project (Grant No. 2014AA01A701) Beijing Natural Science Foundation (Grant No. 4152047)
文摘Hypertext transfer protocol(HTTP) adaptive streaming(HAS) plays a key role in mobile video transmission. Considering the multi-segment and multi-rate features of HAS, this paper proposes a buffer-driven resource management(BDRM) method to enhance HAS quality of experience(QoE) in mobile network. Different from the traditional methods only focusing on base station side without considering the buffer, the proposed method takes both station and client sides into account and end user's buffer plays as the drive of whole schedule process. The proposed HAS QoE influencing factors are composed of initial delay, rebuffering and quality level. The BDRM method decomposes the HAS QoE maximization problem into client and base station sides separately to solve it in multicell and multi-user video playing scene in mobile network. In client side, the decision is made based on buffer probe and rate request algorithm by each user separately. It guarantees the less rebuffering events and decides which HAS segment rate to fetch. While, in the base station side, the schedule of wireless resource is made to maximize the quality level of all access clients and decides the final rate pulled from HAS server. The drive of buffer and twice rate request schemes make BDRMtake full advantage of HAS's multi-segment and multi-rate features. As to the simulation results, compared with proportional fair(PF), Max C/I and traditional HAS schedule(THS) methods, the proposed BDRM method decreases rebuffering percent to 1.96% from 11.1% with PF and from 7.01% with THS and increases the mean MOS of all users to 3.94 from 3.42 with PF method and from 2.15 with Max C/I method. It also guarantees a high fairness with 0.98 from the view of objective and subjective assessment metrics.
