LAUSR

The popularity of immersive multimedia content is prevalent and the consumption of 360° videos is increasing rapidly in varied domains. The broadcast of such content in cellular networks will be challenging in terms of dynamic content adaptation and efficient resource allocation to serve heterogeneous consumers. In this work, we propose an intelligent immersive new radio multimedia broadcast multicast system (NR-MBMS), I2MB, for next-generation cellular networks. I2MB intelligently forecasts the users’ viewing angle and the 360° video tiles to be broadcast beforehand using long short-term memory network. We define broadcast areas by using modified K-means clustering. The complex multivariable optimization problem that integrates efficient adaptive 360-degree video encoding and tiled broadcast using optimized transmission parameters is defined as as a Markov decision process (MDP). In a dense urban scenario with a large MBSFN (multimedia broadcast multicast service single frequency network) synchronization area, the state and action space dimensionality is very high, in which the solution is obtained by using deep deterministic policy gradient (DDPG) algorithm. I2MB incorporates deep reinforcement learning based radio resource allocation (modulation-coding scheme and frequency-time resource blocks) and tiled video encoding to maximize the viewport video quality experienced by the broadcast mobile users. I2MB provides improved immersive video broadcast streaming quality while serving a higher number of mobile users. Adaptive encoding of 360° video tiles and radio resource allocation are performed based on users’ forecasted viewing angle, spatial distribution, channel conditions, and service request. The performance evaluation of our proposed scheme, I2MB, shows considerable gains in viewport quality (46.83%) and number of users served (30.52%), over a recent state-of-the-art method VRCAST.