Towards understanding the complexity of joint QoS and in-network processing (INP) optimization in sensornets, we study the problem of jointly optimizing packet packing and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. We design a distributed, online protocol tPack that schedules packet transmissions to maximize the local utility of packet packing at each node. We evaluate the properties of tPack in NetEye testbed. We find that jointly optimizing data delivery timeliness and packet packing and considering real-world aggregation constraints significantly improve network performance.