In the current age, users consume multimedia content in very heterogeneous scenarios in
terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple
independent streams, each covering a different possible requirement for the clients, with an obvious negative
impact in both storage and computational requirements. These drawbacks can be avoided by using codecs
that enable scalability, i.e., the ability to generate a progressive bitstream, containing a base layer followed by
multiple enhancement layers, that allow decoding the same bitstream serving multiple reconstructions and
visualization specifications. While scalable coding is a well-known and addressed feature in conventional
image and video codecs, this paper focuses on a new and very different problem, notably the development
of scalable coding solutions for deep learning-based Point Cloud (PC) coding. The peculiarities of this 3D
representation make it hard to implement flexible solutions that do not compromise the other functionalities
of the codec. This paper proposes a joint quality and resolution scalability scheme, named Scalable
Resolution and Quality Hyperprior (SRQH), that, contrary to previous solutions, can model the relationship
between latents obtained with models trained for different RD tradeoffs and/or at different resolutions.
Experimental results obtained by integrating SRQH in the emerging JPEG Pleno learning-based PC coding
standard showthat SRQH allows decoding the PC at different qualities and resolutions with a single bitstream
while incurring only in a limited RD penalty and increment in complexity w.r.t. non-scalable JPEG PCC that
would require one bitstream per coding configuration.