Cross Layer Design for Efficient Video Streaming over LTE Using Scalable Video Coding

Abstract

Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) offers high data rate capabilities to mobile users, and network operators are trying to deliver a true mobile broadband experience over LTE networks. Mobile TV and Video on Demand (VoD) are expected to be the main revenue generators in the near future, and efficient video streaming over wireless is the key to achieve this goal. Video services use both unicast and multicast video transmission based on its applications. Enhanced Multimedia Broadcast/Multicast Service (EMBMS) is defined in 3GPP specification to support download delivery and streaming delivery to group users in LTE mobile networks. In 3GPP Release 8 specification, the EMBMS transmission is classified into single-cell transmission and MBSFN (Multicast Broadcast Single Frequency Network) transmission. In this paper, we propose a Scalable Video Coding (SVC) based video streaming scheme with dynamic adaptations and a scheduling scheme based on channel quality for unicast and multicast video transmissions. In our proposed unicast scheme, Channel Quality Indicator (CQI) feedbacks from User Equipment’s (UE) are used for dynamic adaptations and scheduling. Cross layer signaling between Medium Access Control (MAC) and Real Time Transport (RTP) protocols is used to achieve the channel dependent adaptation for unicast in video server. Simulation results for unicast indicate improved video quality for more number of users with reduced bit rate video traffic. Approximately 13% video quality gain is observed for users at the cell edge using this adaptation scheme. We have also proposed a scheduling scheme for multicast over MBSFN networks. This scheme optimizes the radio spectrum allocation by adaptive modulation and coding (AMC) and frequency scheduling based on distribution of users in different channel quality regions. Through simulations we demonstrate that spectrum savings in the order of 72 to 82% is achievable in different user distribution scenarios with our proposed scheme.