Concerning the problem that AutoRegressive Integrated Moving Average (ARIMA) model and Long Short-Term Memory (LSTM) unit do not utilize the collaboration between Base Stations (BSs) in traffic prediction, a new method called Traffic Prediction based on Space Collaboration (TPBC) which uses the collaboration between BSs produced by users was proposed. Firstly, a BS cooperative network was constructed based on the collaboration between BSs and then divided into multiple communities. Next, the cooperative BSs, which have the closest relationships with the target BS in the same community, were found via Granger causality test. Finally, a hybrid neural network was constructed by LSTM and Embedding layer, and the historial traffic of target BS and each cooperative BS was utilized for traffic prediction of target BS. The experimental results show that the Root Mean Square Error (RMSE) of TPBC is reduced by 29.19% and 27.47% compared with ARIMA and LSTM respectively. It shows that TPBC has the capability of improving the accuracy of BS traffic prediction effectively, which benefits traffic offloading and energy saving.

To the Peer-to-Peer (P2P) streaming application based on network coding, a load transfer-based node bandwidth resource balancing policy was proposed to avoid the overloaded node, which was due to random neighbor node selection and bandwidth resource requestion. When some nodes overloaded, the nodes with spare bandwidth resource could be selected as load transfer nodes to alleviate the load of overloaded peers. Through the ladder-based bandwidth allocation mechanism, the coded data required by the request nodes could be pushed to the selected load transfer nodes. The numerical simulation results show that the proposed load transfer policy can efficiently alleviate the bandwith resource consumption of overloaded nodes, and the hot area of network can be avoided.