To attract more users, mobile network operators may offer both chargeable/metered and free services as parts of their product bundles. However, an extra cost of providing the free services and their substitution effect on metered services in the bundles may reduce their profitability. Therefore, the mobile network operators must decide on including free services in their offered bundles based on the profitability. In this paper, we develop an analytical framework composed of leader-follower game models to obtain the Stackelberg equilibrium strategy of the mobile network operators and analyze several important properties of the equilibrium. The proposed framework takes into account the congestion effect resulting from incorporating free services in the bundles. The game models are general and capable of analyzing different setting of bundles. Extensive performance evaluations show that the inclusion of free services can improve the profit of the mobile network operators when (i) the users' interest for the free services is above a certain value, (ii) the free services add small cost to the bundle, and (iii) the users' demand of free services is less dependent on that of metered services in the bundle.

Zero-rating is the concept of not counting data usage of end-users for accessing the basic functions of some websites via mobile network operators (MNOs). Recently, some zero-rating platforms (e.g. Internet.org) are formed allowing the websites present their contents as zero-rated. In current setup of zero-rating platforms, no monetary reward is transferred to partner MNOs. Therefore, MNOs face a question of whether to cooperate with zero-rating platforms or not. In this paper, a variant of Stackelberg game is used to model the interaction between an MNO and its end-users toward providing their access to zero-rating services (i.e. partnership of the MNO and zero-rating platforms). We propose a bundle pricing scheme for the MNO and study the effects of including zero-rating services in the bundles. The proposed model assists the MNO to make decision on including zero-rating services in its bundles as well as appropriate pricing scheme. Numerical studies show that depending on the end- users' preferences, the MNO may increase its profit by including zero-rating services in its bundles.

Optical Packet Loss Rate (OPLR) is a critical performance measure for Optical Packet Switched (OPS) networks. In this paper, we use a common Artificial Neural Network (ANN), that is multilayer perceptron (MLP), as a fast OPLR estimator for bufferless slotted OPS networks. Based on the proposed estimator, an algorithm called OPLRC is proposed to control Optical Flow's Packet Loss Rates (OFPLRs) which are OPLRs for optical packets transmitted from different ingress switches. OPLRC is a greedy algorithm which uses estimated OFPLRs of the trained MLP to tag some optical packets at ingress switches as eligible for drop at core switches in case of contention. This will control OFPLRs of un-tagged optical packets at the specified limits while giving some chance for tagged optical packets to reach their destinations. The accuracy of the proposed estimator is analyzed using numerical methods and extensive simulations. Results show that OPLRC can manage OFPLRs of untagged optical packets with an acceptable accuracy.